From 4938453ed71a0b0f734b0aeb0cec7076d885f8d5 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Fri, 4 Sep 2026 21:24:17 +0200 Subject: [PATCH 01/57] timeout in jolt enemy tank --- arenai_agent/src/core/train_environment.cpp | 48 +++++++------------ arenai_agent/src/core/train_environment.h | 4 -- arenai_core/include/arenai_core/environment.h | 5 +- arenai_core/src/environment.cpp | 11 +++-- arenai_core/tests/src/tests_environment.cpp | 36 +++++++++----- arenai_desktop/src/core/game_environment.cpp | 4 +- arenai_model/include/arenai_model/constants.h | 4 +- arenai_model/include/arenai_model/tank.h | 1 + .../include/arenai_model/tank_factory.h | 3 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 33 ++++++++++--- arenai_model/src/tank/jolt_enemy_tank.h | 12 ++++- arenai_model/src/tank/jolt_tank_factory.cpp | 6 ++- arenai_model/src/tank/jolt_tank_factory.h | 3 +- .../src/tests_enemy_tank/tests_enemy_tank.cpp | 40 ++++++++++------ .../tests_player_tank/tests_player_tank.cpp | 5 +- .../tests_proprioception.cpp | 12 ++--- .../tests/src/tests_reward/tests_reward.cpp | 47 +++++++++++------- .../tests/src/tests_shell/tests_shell.cpp | 45 +++++++++++------ 18 files changed, 197 insertions(+), 122 deletions(-) diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index bd574fe1..68436c91 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -26,13 +26,10 @@ namespace arenai::agent { const int vision_num_threads) : BaseTanksEnvironment( std::make_shared(android_assets_path), graphics_backend, - nb_tanks, wanted_frequency, vision_height, vision_width, vision_num_threads, false), - wanted_frequency(wanted_frequency), - max_frames_without_hit(static_cast(30.f / wanted_frequency)), - remaining_frames(nb_tanks, max_frames_without_hit), - nb_frames_added_when_hit(static_cast(3.f / wanted_frequency)), - nb_frames_added_when_kill(static_cast(15.f / wanted_frequency)), nb_tanks(nb_tanks), - nb_steps(0), done(nb_tanks, false), already_done(nb_tanks, false), + nb_tanks, wanted_frequency, vision_height, vision_width, vision_num_threads, false, + true, static_cast(max_episode_steps) * wanted_frequency), + wanted_frequency(wanted_frequency), nb_tanks(nb_tanks), nb_steps(0), + done(nb_tanks, false), already_done(nb_tanks, false), max_episode_steps(max_episode_steps), nb_hits_per_tanks(nb_tanks, 0), nb_kills_per_tanks(nb_tanks, 0), reward_metric(std::make_shared( "r", 4 * nb_tanks * max_episode_steps, 1, true)), @@ -97,6 +94,13 @@ namespace arenai::agent { return is_suicide_result; }); + const auto is_timeout = apply_on_enemies>([&](const auto &factories) { + std::vector is_timeout_result; + is_timeout_result.reserve(nb_tanks); + for (const auto &factory: factories) is_timeout_result.push_back(factory->is_timeout()); + return is_timeout_result; + }); + // fire / hit frequencies (per second, per tank that acted this step) int nb_acting = 0, nb_fires = 0, nb_hits = 0; for (int i = 0; i < nb_tanks; i++) { @@ -113,32 +117,15 @@ namespace arenai::agent { static_cast(nb_hits) / (static_cast(nb_acting) * wanted_frequency)); } - // step over tanks (remaining steps, hits and kills counters + detect and apply timeout + detect death) + // step over tanks (hits and kills counters) for (int i = 0; i < step_result.size(); i++) { - remaining_frames[i]--; - - if (has_hit[i]) { - remaining_frames[i] += nb_frames_added_when_hit; - nb_hits_per_tanks[i] += 1; - } - if (has_kill[i]) { - remaining_frames[i] += nb_frames_added_when_kill; - nb_kills_per_tanks[i] += 1; - } - - const auto &[state, reward, is_done] = step_result[i]; - - // detect death (kill or suicide) - if (is_done) { - if (!already_done[i] && !is_suicide[i]) nb_kills_episode++; - done[i] = true; - } - // starving out (no hit for too long) is a real death: penalized and terminal - if (!done[i] && remaining_frames[i] <= 0) { - constexpr float timeout_penalty = 1.f; + if (has_hit[i]) { nb_hits_per_tanks[i] += 1; } + if (has_kill[i]) { nb_kills_per_tanks[i] += 1; } - step_result[i] = {state, reward - timeout_penalty, true}; + // detect death (kill, suicide or timeout) + if (const auto &[state, reward, is_done] = step_result[i]; is_done) { + if (!already_done[i] && !is_suicide[i] && !is_timeout[i]) nb_kills_episode++; done[i] = true; } } @@ -190,7 +177,6 @@ namespace arenai::agent { void TrainTankEnvironment::on_reset_physics( const std::unique_ptr &engine) { - remaining_frames = std::vector(nb_tanks, max_frames_without_hit); // close the previous episode's counter (skip the very first reset) if (nb_steps > 0) kill_metric->add(static_cast(nb_kills_episode)); diff --git a/arenai_agent/src/core/train_environment.h b/arenai_agent/src/core/train_environment.h index 070c30fe..aa462064 100644 --- a/arenai_agent/src/core/train_environment.h +++ b/arenai_agent/src/core/train_environment.h @@ -38,10 +38,6 @@ namespace arenai::agent { private: float wanted_frequency; - int max_frames_without_hit; - std::vector remaining_frames; - int nb_frames_added_when_hit; - int nb_frames_added_when_kill; int nb_tanks; int nb_steps; diff --git a/arenai_core/include/arenai_core/environment.h b/arenai_core/include/arenai_core/environment.h index a1dfca97..393a442d 100644 --- a/arenai_core/include/arenai_core/environment.h +++ b/arenai_core/include/arenai_core/environment.h @@ -27,7 +27,7 @@ namespace arenai::core { const std::shared_ptr &file_reader, const std::shared_ptr &graphics_backend, int nb_tanks, float wanted_frequency, int vision_height, int vision_width, int vision_num_threads, - bool vision_thread_sleep); + bool vision_thread_sleep, bool apply_timeout, float max_episode_seconds); virtual std::vector> step(float time_delta, const std::vector &actions); @@ -60,6 +60,9 @@ namespace arenai::core { bool drawing_started_; + bool apply_timeout; + float max_episode_seconds; + std::shared_ptr graphics_backend; std::shared_ptr gl_context; diff --git a/arenai_core/src/environment.cpp b/arenai_core/src/environment.cpp index bc55b76a..d34f9f2f 100644 --- a/arenai_core/src/environment.cpp +++ b/arenai_core/src/environment.cpp @@ -18,7 +18,8 @@ namespace arenai::core { const std::shared_ptr &file_reader, const std::shared_ptr &graphics_backend, const int nb_tanks, float wanted_frequency, const int vision_height, const int vision_width, - const int vision_num_threads, const bool vision_thread_sleep) + const int vision_num_threads, const bool vision_thread_sleep, const bool apply_timeout, + const float max_episode_seconds) : wanted_frequency(wanted_frequency), nb_tanks(nb_tanks), vision_height(vision_height), vision_width(vision_width), vision_num_threads(vision_num_threads), vision_thread_sleep(vision_thread_sleep), @@ -27,8 +28,9 @@ namespace arenai::core { vision_thread_sleep)), physic_engine(model::make_physic_engine(wanted_frequency)), nb_reset_frames(static_cast(4.f / wanted_frequency)), drawing_started_(false), - graphics_backend(graphics_backend), gl_context(graphics_backend->render_context()), - rng(dev()), file_reader(file_reader) {} + apply_timeout(apply_timeout), graphics_backend(graphics_backend), + gl_context(graphics_backend->render_context()), rng(dev()), file_reader(file_reader), + max_episode_seconds(max_episode_seconds) {} std::vector> BaseTanksEnvironment::step(const float time_delta, const std::vector &actions) { @@ -88,7 +90,8 @@ namespace arenai::core { for (int i = 0; i < nb_tanks; i++) { tanks.push_back(tank_factory->make_enemy_tank( file_reader, "enemy_" + std::to_string(i), - glm::vec3(x_pos_u_dist(rng), 0.f, y_pos_u_dist(rng)))); + glm::vec3(x_pos_u_dist(rng), 0.f, y_pos_u_dist(rng)), apply_timeout, + max_episode_seconds)); tank_controller_handler.push_back(std::make_unique( wanted_frequency, model::ENEMY_TURRET_RADIAL_VELOCITY)); diff --git a/arenai_core/tests/src/tests_environment.cpp b/arenai_core/tests/src/tests_environment.cpp index 21533823..73c86d11 100644 --- a/arenai_core/tests/src/tests_environment.cpp +++ b/arenai_core/tests/src/tests_environment.cpp @@ -26,7 +26,8 @@ TEST_F(EnvironmentTest, ResetReturnsCorrectNumberOfStates) { constexpr int vision_w = 16; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false); + file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false, + 60.f); const auto states = env.reset(100.f, 100.f); @@ -46,7 +47,8 @@ TEST_F(EnvironmentTest, ResetInitialVisionIsNotBlack) { constexpr int vision_w = 16; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false); + file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false, + 60.f); for (const auto states = env.reset(100.f, 100.f); const auto &[vision, proprioception]: states) { @@ -83,7 +85,8 @@ TEST_F(EnvironmentTest, ResetGoldenImage) { constexpr int vision_w = 16; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false); + file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false, + 60.f); env.seed(42); @@ -147,7 +150,8 @@ TEST_F(EnvironmentTest, ResetProprioceptionNonEmpty) { constexpr int nb_tanks = 2; constexpr float frequency = 1.f / 60.f; - TestTanksEnvironment env(file_reader, graphics_backend, nb_tanks, frequency, 8, 8, 1, false); + TestTanksEnvironment env( + file_reader, graphics_backend, nb_tanks, frequency, 8, 8, 1, false, false, 60.f); for (const auto states = env.reset(100.f, 100.f); const auto &[vision, proprioception]: states) { @@ -165,7 +169,8 @@ TEST_F(EnvironmentTest, ResetCallsOnResetPhysics) { constexpr int nb_tanks = 1; constexpr float frequency = 1.f / 60.f; - TestTanksEnvironment env(file_reader, graphics_backend, nb_tanks, frequency, 8, 8, 1, false); + TestTanksEnvironment env( + file_reader, graphics_backend, nb_tanks, frequency, 8, 8, 1, false, false, 60.f); env.reset(100.f, 100.f); @@ -185,7 +190,8 @@ TEST_F(EnvironmentTest, StepReturnsCorrectNumberOfTuples) { constexpr int vision_w = 16; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false); + file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false, + 60.f); env.reset(100.f, 100.f); @@ -209,7 +215,8 @@ TEST_F(EnvironmentTest, StepRewardAndDoneAreValid) { constexpr int nb_tanks = 2; constexpr float frequency = 1.f / 60.f; - TestTanksEnvironment env(file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false); + TestTanksEnvironment env( + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); env.reset(100.f, 100.f); @@ -235,7 +242,8 @@ TEST_F(EnvironmentTest, StepCallsOnDraw) { constexpr int nb_tanks = 1; constexpr float frequency = 1.f / 60.f; - TestTanksEnvironment env(file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false); + TestTanksEnvironment env( + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); env.reset(100.f, 100.f); @@ -263,7 +271,8 @@ TEST_F(EnvironmentTest, MultipleStepsDoNotCrash) { constexpr int nb_tanks = 2; constexpr float frequency = 1.f / 60.f; - TestTanksEnvironment env(file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false); + TestTanksEnvironment env( + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); env.reset(100.f, 100.f); @@ -288,7 +297,8 @@ TEST_F(EnvironmentTest, ResetCallsOnResetDrawables) { constexpr int nb_tanks = 1; constexpr float frequency = 1.f / 60.f; - TestTanksEnvironment env(file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false); + TestTanksEnvironment env( + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); env.reset(100.f, 100.f); @@ -305,7 +315,8 @@ TEST_F(EnvironmentTest, FullLifecycle) { constexpr int nb_tanks = 2; constexpr float frequency = 1.f / 60.f; - TestTanksEnvironment env(file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false); + TestTanksEnvironment env( + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); // First episode const auto initial_states = env.reset(100.f, 100.f); @@ -334,7 +345,8 @@ TEST_F(EnvironmentTest, StopDrawingDoubleCallDoesNotCrash) { constexpr int nb_tanks = 1; constexpr float frequency = 1.f / 60.f; - TestTanksEnvironment env(file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false); + TestTanksEnvironment env( + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); env.reset(100.f, 100.f); diff --git a/arenai_desktop/src/core/game_environment.cpp b/arenai_desktop/src/core/game_environment.cpp index 57c09e13..9a78551f 100644 --- a/arenai_desktop/src/core/game_environment.cpp +++ b/arenai_desktop/src/core/game_environment.cpp @@ -25,7 +25,9 @@ namespace arenai::desktop { : BaseTanksEnvironment( std::make_shared(asset_folder_path), view::make_vulkan_backend(settings.vision_gpu), settings.nb_tanks, wanted_frequency, - vision_height, vision_width, 8, true), + // no starving timeout in the playable game; 180s matches the training + // normalization of the remaining-time proprioception (5400 steps at 30Hz) + vision_height, vision_width, 8, true, false, 180.f), windowed_backend(graphics_backend), asset_file_reader(std::make_shared(asset_folder_path)), player_tank(std::nullptr_t()), player_renderer(std::nullptr_t()), diff --git a/arenai_model/include/arenai_model/constants.h b/arenai_model/include/arenai_model/constants.h index fca7ecdf..c87e30c4 100644 --- a/arenai_model/include/arenai_model/constants.h +++ b/arenai_model/include/arenai_model/constants.h @@ -11,8 +11,8 @@ namespace arenai::model { constexpr float WHEEL_RADIAL_VELOCITY = std::numbers::pi * 5.f; - // (pos, vel, forward, up, ang_vel) * (9 items: 6 wheels, 1 chassis, 1 turret, 1 canon) - chassis pos + remaining shells ratio + cooldown ratio - constexpr int ENEMY_PROPRIOCEPTION_SIZE = (3 + 3 + 3 + 3 + 3) * (6 + 3) - 3 + 1 + 1; + // (pos, vel, forward, up, ang_vel) * (9 items: 6 wheels, 1 chassis, 1 turret, 1 canon) - chassis pos + remaining shells ratio + cooldown ratio + timeout ratio + constexpr int ENEMY_PROPRIOCEPTION_SIZE = (3 + 3 + 3 + 3 + 3) * (6 + 3) - 3 + 1 + 1 + 1; constexpr int ENEMY_NB_CONTINUOUS_ACTION = 2 + 2; constexpr int ENEMY_NB_DISCRETE_ACTION = 2; diff --git a/arenai_model/include/arenai_model/tank.h b/arenai_model/include/arenai_model/tank.h index 6fea5749..a29ed7d3 100644 --- a/arenai_model/include/arenai_model/tank.h +++ b/arenai_model/include/arenai_model/tank.h @@ -65,6 +65,7 @@ namespace arenai::model { virtual bool is_first_frame_dead() const = 0; virtual bool is_suicide() const = 0; + virtual bool is_timeout() const = 0; virtual void on_death() = 0; }; diff --git a/arenai_model/include/arenai_model/tank_factory.h b/arenai_model/include/arenai_model/tank_factory.h index 9da73508..096eb640 100644 --- a/arenai_model/include/arenai_model/tank_factory.h +++ b/arenai_model/include/arenai_model/tank_factory.h @@ -22,7 +22,8 @@ namespace arenai::model { virtual std::unique_ptr make_enemy_tank( const std::shared_ptr &file_reader, - const std::string &tank_prefix_name, glm::vec3 chassis_pos) = 0; + const std::string &tank_prefix_name, glm::vec3 chassis_pos, bool apply_timeout, + float max_episode_seconds) = 0; virtual std::unique_ptr make_player_tank( const std::shared_ptr &file_reader, diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 89c0167f..a256cf4c 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -45,7 +45,7 @@ namespace arenai::model { JoltPhysicEngine &engine, const std::shared_ptr &file_reader, const std::string &tank_prefix_name, const glm::vec3 chassis_pos, - const float wanted_frame_frequency) + const float wanted_frame_frequency, const bool apply_timeout, float max_episode_seconds) : JoltTank( engine, file_reader, tank_prefix_name, chassis_pos, wanted_frame_frequency, [this](const ShellItem *shell, const ShellContactInfo &info, Item *item) { @@ -64,8 +64,13 @@ namespace arenai::model { fire_cooldown_frames(static_cast(1.f / 6.f / wanted_frame_frequency)), curr_cooldown_frame(fire_cooldown_frames), shells_recharged_per_hit(5), nb_frames_per_shell_regen(static_cast(1.5f / wanted_frame_frequency)), - curr_frame_shell_regen(0), is_dead_already_triggered(false), has_touch(false), - has_kill(false), has_fired(false) {} + curr_frame_shell_regen(0), is_dead_already_triggered(false), apply_timeout(apply_timeout), + max_frames_without_hit(static_cast(30.f / wanted_frame_frequency)), + remaining_frames(max_frames_without_hit), + nb_frames_added_when_hit(static_cast(3.f / wanted_frame_frequency)), + nb_frames_added_when_kill(static_cast(15.f / wanted_frame_frequency)), + has_hit(false), has_kill(false), has_fired(false), + max_episode_frames(static_cast(max_episode_seconds / wanted_frame_frequency)) {} float JoltEnemyTank::compute_hit_reward( const glm::vec3 &fire_pos, const glm::vec3 &enemy_pos, const glm::vec3 &shell_pos) const { @@ -212,6 +217,15 @@ namespace arenai::model { // 4. fire cooldown curr_cooldown_frame = std::min(fire_cooldown_frames, curr_cooldown_frame + 1); + + // 5. timeout + remaining_frames--; + + if (has_hit) remaining_frames += nb_frames_added_when_hit; + if (has_kill) remaining_frames += nb_frames_added_when_kill; + + // starving out (no hit for too long) is a real death: penalized and terminal + if (apply_timeout && remaining_frames <= 0) kill_life_items(); } void JoltEnemyTank::on_shell_fired(const std::shared_ptr &shell) { @@ -246,11 +260,11 @@ namespace arenai::model { hit = true; killed = true; - has_touch = true; + has_hit = true; has_kill = true; } else if (!life_item->is_dead()) { hit = true; - has_touch = true; + has_hit = true; } } @@ -276,8 +290,8 @@ namespace arenai::model { } bool JoltEnemyTank::consume_has_hit() { - if (has_touch) { - has_touch = false; + if (has_hit) { + has_hit = false; return true; } return false; @@ -302,6 +316,8 @@ namespace arenai::model { return curr_frame_upside_down > max_frames_upside_down; } + bool JoltEnemyTank::is_timeout() const { return apply_timeout && remaining_frames <= 0; } + void JoltEnemyTank::on_death() { if (is_dead() && !is_dead_already_triggered) { is_dead_already_triggered = true; @@ -353,6 +369,9 @@ namespace arenai::model { result.push_back(static_cast(nb_shells) / static_cast(max_shells)); result.push_back( static_cast(curr_cooldown_frame) / static_cast(fire_cooldown_frames)); + result.push_back(std::clamp( + static_cast(remaining_frames) / static_cast(max_episode_frames), 0.f, + 1.f)); return result; } diff --git a/arenai_model/src/tank/jolt_enemy_tank.h b/arenai_model/src/tank/jolt_enemy_tank.h index 3cadf86e..3abb9792 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.h +++ b/arenai_model/src/tank/jolt_enemy_tank.h @@ -41,13 +41,14 @@ namespace arenai::model { JoltPhysicEngine &engine, const std::shared_ptr &file_reader, const std::string &tank_prefix_name, glm::vec3 chassis_pos, - float wanted_frame_frequency); + float wanted_frame_frequency, bool apply_timeout, float max_episode_seconds); float get_reward() const override; bool is_dead() const override; bool is_first_frame_dead() const override; bool is_suicide() const override; + bool is_timeout() const override; bool consume_has_hit() override; bool consume_has_kill() override; @@ -92,7 +93,14 @@ namespace arenai::model { bool is_dead_already_triggered; - bool has_touch; + bool apply_timeout; + int max_frames_without_hit; + int remaining_frames; + int nb_frames_added_when_hit; + int nb_frames_added_when_kill; + int max_episode_frames; + + bool has_hit; bool has_kill; bool has_fired; diff --git a/arenai_model/src/tank/jolt_tank_factory.cpp b/arenai_model/src/tank/jolt_tank_factory.cpp index 8c557376..9aae042a 100644 --- a/arenai_model/src/tank/jolt_tank_factory.cpp +++ b/arenai_model/src/tank/jolt_tank_factory.cpp @@ -17,9 +17,11 @@ namespace arenai::model { std::unique_ptr JoltTankFactory::make_enemy_tank( const std::shared_ptr &file_reader, - const std::string &tank_prefix_name, glm::vec3 chassis_pos) { + const std::string &tank_prefix_name, glm::vec3 chassis_pos, bool apply_timeout, + float max_episode_seconds) { return std::make_unique( - engine, file_reader, tank_prefix_name, chassis_pos, wanted_frame_frequency); + engine, file_reader, tank_prefix_name, chassis_pos, wanted_frame_frequency, + apply_timeout, max_episode_seconds); } std::unique_ptr JoltTankFactory::make_player_tank( diff --git a/arenai_model/src/tank/jolt_tank_factory.h b/arenai_model/src/tank/jolt_tank_factory.h index ce260feb..acc4b249 100644 --- a/arenai_model/src/tank/jolt_tank_factory.h +++ b/arenai_model/src/tank/jolt_tank_factory.h @@ -17,7 +17,8 @@ namespace arenai::model { std::unique_ptr make_enemy_tank( const std::shared_ptr &file_reader, - const std::string &tank_prefix_name, glm::vec3 chassis_pos) override; + const std::string &tank_prefix_name, glm::vec3 chassis_pos, bool apply_timeout, + float max_episode_seconds) override; std::unique_ptr make_player_tank( const std::shared_ptr &file_reader, diff --git a/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp b/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp index 74e7523e..8eff76d0 100644 --- a/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp +++ b/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp @@ -22,7 +22,8 @@ using namespace arenai::controller; TEST_F(EnemyTankTest, DeadWhenSingleWheelDestroyed) { add_ground(); - const auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); + const auto tank = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -53,7 +54,7 @@ TEST_F(EnemyTankTest, DeadWhenSingleWheelDestroyed) { TEST_F(EnemyTankTest, OnDeathMultipleCallsDoNotCrash) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -77,7 +78,7 @@ TEST_F(EnemyTankTest, OnDeathMultipleCallsDoNotCrash) { TEST_F(EnemyTankTest, OnDeathBeforeDeathDoesNothing) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -93,8 +94,10 @@ TEST_F(EnemyTankTest, OnDeathBeforeDeathDoesNothing) { TEST_F(EnemyTankTest, RewardWhenAllEnemiesDeadAndShellFired) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -129,7 +132,7 @@ TEST_F(EnemyTankTest, RewardWhenAllEnemiesDeadAndShellFired) { TEST_F(EnemyTankTest, RewardNoNaNWhenAloneInTankList) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -156,7 +159,7 @@ TEST_F(EnemyTankTest, RewardNoNaNWhenAloneInTankList) { TEST_F(EnemyTankTest, ShellHitsGroundNoRewardNoCrash) { add_ground(); // point the tank away from any enemy so the shell hits the ground - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -194,7 +197,7 @@ TEST_F(EnemyTankTest, ShellHitsGroundNoRewardNoCrash) { TEST_F(EnemyTankTest, SuicideDetectionWhenFlipped) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -214,8 +217,10 @@ TEST_F(EnemyTankTest, SuicideDetectionWhenFlipped) { TEST_F(EnemyTankTest, HasHitOtherTankResetsAfterCall) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -250,10 +255,11 @@ TEST_F(EnemyTankTest, ShellReserveRegeneratesOverTime) { constexpr int initial_shells = 10; constexpr float seconds_per_regen = 1.5f; - constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 2; + // proprioception ends with (reserve, cooldown, timeout) ratios + constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 3; add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); float last_reserve = tank->get_proprioception()[proprioception_reserve_index]; ASSERT_FLOAT_EQ( @@ -308,8 +314,11 @@ TEST_F(EnemyTankTest, ShellReserveRegenerationIsCappedAtMaximalReserve) { constexpr int max_shells = 30; constexpr float seconds_per_regen = 1.5f; + // proprioception ends with (reserve, cooldown, timeout) ratios + constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 3; + add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); for (int i = 0; i < static_cast(nb_frames_one_second * seconds_per_regen * max_shells); i++) { @@ -317,7 +326,8 @@ TEST_F(EnemyTankTest, ShellReserveRegenerationIsCappedAtMaximalReserve) { tank->tick({}); } - ASSERT_FLOAT_EQ(tank->get_proprioception().back(), 1.f) << "the reserve should be full"; + ASSERT_FLOAT_EQ(tank->get_proprioception()[proprioception_reserve_index], 1.f) + << "the reserve should be full"; const std::shared_ptr shared_tank(tank.release()); const std::vector tanks{shared_tank}; @@ -325,6 +335,6 @@ TEST_F(EnemyTankTest, ShellReserveRegenerationIsCappedAtMaximalReserve) { // the reserve is already full: several periods must not push it above it for (int i = 0; i < 5 * nb_frames_one_second; i++) shared_tank->tick(tanks); - ASSERT_FLOAT_EQ(shared_tank->get_proprioception().back(), 1.f) + ASSERT_FLOAT_EQ(shared_tank->get_proprioception()[proprioception_reserve_index], 1.f) << "regeneration should never take the reserve above its max value"; } diff --git a/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp b/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp index 6ff0f73e..babe512f 100644 --- a/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp +++ b/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp @@ -29,7 +29,7 @@ TEST_F(PlayerTankTest, ScoreZeroAtCreation) { TEST_F(PlayerTankTest, ScoreIncreasesOnHit) { add_ground(); const auto player = tank_factory->make_player_tank(file_reader, "player", {0.f, 5.f, 0.f}); - auto enemy = tank_factory->make_enemy_tank(file_reader, "enemy", {0.f, 5.f, 30.f}); + auto enemy = tank_factory->make_enemy_tank(file_reader, "enemy", {0.f, 5.f, 30.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -47,7 +47,8 @@ TEST_F(PlayerTankTest, ScoreIncreasesOnHit) { TEST_F(PlayerTankTest, ScoreHigherOnKillThanHit) { add_ground(); const auto player = tank_factory->make_player_tank(file_reader, "player", {0.f, 5.f, 0.f}); - const auto enemy = tank_factory->make_enemy_tank(file_reader, "enemy", {0.f, 5.f, 30.f}); + const auto enemy = + tank_factory->make_enemy_tank(file_reader, "enemy", {0.f, 5.f, 30.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); diff --git a/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp b/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp index 3d6ae9d4..143557eb 100644 --- a/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp +++ b/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp @@ -18,7 +18,7 @@ using namespace arenai::utils; TEST_F(ProprioceptionTest, ProprioceptionSizeCorrect) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -30,7 +30,7 @@ TEST_F(ProprioceptionTest, ProprioceptionSizeCorrect) { TEST_F(ProprioceptionTest, ProprioceptionNoNaN) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -45,7 +45,7 @@ TEST_F(ProprioceptionTest, ProprioceptionNoNaN) { TEST_F(ProprioceptionTest, ProprioceptionNoInfinity) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); for (int i = 0; i < 10; i++) engine->step(1.f / 60.f); @@ -59,7 +59,7 @@ TEST_F(ProprioceptionTest, ProprioceptionNoInfinity) { TEST_F(ProprioceptionTest, ProprioceptionContainsSubItemRelativePositions) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -89,7 +89,7 @@ TEST_F(ProprioceptionTest, ProprioceptionContainsSubItemRelativePositions) { TEST_F(ProprioceptionTest, ProprioceptionConsistentSize) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -106,7 +106,7 @@ TEST_F(ProprioceptionTest, ProprioceptionConsistentSize) { TEST_F(ProprioceptionTest, ProprioceptionForwardAndUpVectorsValid) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); diff --git a/arenai_model/tests/src/tests_reward/tests_reward.cpp b/arenai_model/tests/src/tests_reward/tests_reward.cpp index f2ac504d..c72a2879 100644 --- a/arenai_model/tests/src/tests_reward/tests_reward.cpp +++ b/arenai_model/tests/src/tests_reward/tests_reward.cpp @@ -21,8 +21,10 @@ using namespace arenai::controller; TEST_F(RewardTest, RewardZeroWhenAliveNoShot) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -40,8 +42,10 @@ TEST_F(RewardTest, RewardZeroWhenAliveNoShot) { TEST_F(RewardTest, RewardNegativeWhenDead) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -68,8 +72,10 @@ TEST_F(RewardTest, RewardNegativeWhenDead) { TEST_F(RewardTest, DeathPenaltyIsMinusOne) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -98,8 +104,10 @@ TEST_F(RewardTest, DeathPenaltyIsMinusOne) { TEST_F(RewardTest, RewardPositiveOnHit) { add_ground(); // spawn tanks high enough so all parts start above ground and settle cleanly - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); // settle on ground (300 frames = 5s at 60fps) for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -136,8 +144,10 @@ TEST_F(RewardTest, RewardPositiveOnHit) { TEST_F(RewardTest, RewardUnderOneAfterHit) { add_ground(); // spawn tanks high enough so all parts start above ground and settle cleanly - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); // settle on ground (300 frames = 5s at 60fps) for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -198,12 +208,15 @@ TEST_F(RewardTest, RewardUnderOneAfterHit) { TEST_F(RewardTest, NoRewardWhenShootingAWreck) { - constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 2; + // proprioception ends with (reserve, cooldown, timeout) ratios + constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 3; add_ground(); // spawn tanks high enough so all parts start above ground and settle cleanly - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); // settle on ground (300 frames = 5s at 60fps) for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -249,8 +262,10 @@ TEST_F(RewardTest, NoRewardWhenShootingAWreck) { TEST_F(RewardTest, NoKillRewardWhenHittingAnotherPartOfAWreck) { add_ground(); // spawn tanks high enough so all parts start above ground and settle cleanly - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); // settle on ground (300 frames = 5s at 60fps) for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -300,7 +315,7 @@ TEST_F(RewardTest, NoKillRewardWhenHittingAnotherPartOfAWreck) { TEST_F(RewardTest, ZeroRewardWithEmptyTankList) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); diff --git a/arenai_model/tests/src/tests_shell/tests_shell.cpp b/arenai_model/tests/src/tests_shell/tests_shell.cpp index 0819d6f8..f7cacf10 100644 --- a/arenai_model/tests/src/tests_shell/tests_shell.cpp +++ b/arenai_model/tests/src/tests_shell/tests_shell.cpp @@ -19,7 +19,8 @@ using namespace arenai::controller; TEST_F(ShellTest, FireCreatesShellItem) { add_ground(); - const auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + const auto tank = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -40,7 +41,8 @@ TEST_F(ShellTest, FireCreatesShellItem) { TEST_F(ShellTest, ShellDestroyedAfterLifetime) { add_ground(); - const auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + const auto tank = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -68,8 +70,10 @@ TEST_F(ShellTest, ShellDestroyedAfterLifetime) { TEST_F(ShellTest, ShellHitsEnemyTank) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -89,8 +93,10 @@ TEST_F(ShellTest, ShellHitsEnemyTank) { TEST_F(ShellTest, ShellDestroyedOnContact) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -119,7 +125,8 @@ TEST_F(ShellTest, ShellDestroyedOnContact) { TEST_F(ShellTest, NoFireNoNewItems) { add_ground(); - const auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + const auto tank = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); engine->step(1.f / 60.f); @@ -140,8 +147,10 @@ TEST_F(ShellTest, NoFireNoNewItems) { TEST_F(ShellTest, ShellContactCallbackSetsReward) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -202,8 +211,10 @@ namespace { TEST_F(ShellTest, ShellImpactDealsExactlyOneDamage) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -226,8 +237,10 @@ TEST_F(ShellTest, ShellImpactDealsExactlyOneDamage) { TEST_F(ShellTest, TankSurvivesUntilAPartsHealthPointsAreSpent) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -265,8 +278,10 @@ TEST_F(ShellTest, TankSurvivesUntilAPartsHealthPointsAreSpent) { TEST_F(ShellTest, SpentShellDealsNoFurtherDamage) { add_ground(); - auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); - auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); From 6c7483ae5ff68a0b6b74d06cda66b4c613064987 Mon Sep 17 00:00:00 2001 From: Samuel Date: Fri, 4 Sep 2026 21:40:02 +0200 Subject: [PATCH 02/57] * curriculum learning on spawn zone * received hit : -0.3 --- arenai_agent/src/main.cpp | 4 ++-- arenai_model/src/tank/jolt_enemy_tank.cpp | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index 065de690..7d4eb989 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -36,8 +36,8 @@ int main(const int argc, char **argv) { parser.add_argument("--vision_width").scan<'i', int>().default_value(256); parser.add_argument("--initial_spawn_width").scan<'g', float>().default_value(500.f); parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(500.f); - parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(500.f); - parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(500.f); + parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(2000.f); + parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(2000.f); parser.add_argument("--vision_num_threads") .scan<'i', int>() .default_value(static_cast(std::thread::hardware_concurrency())); diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index a256cf4c..964243eb 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -59,7 +59,7 @@ namespace arenai::model { }), max_frames_upside_down(static_cast(4.f / wanted_frame_frequency)), curr_frame_upside_down(0), miss_distance_scale(1.5f), miss_distance_exponent(1.f / 2.f), - hit_reward_scale(0.1f), hit_received_cost(0.15f), initial_nb_shells(10), + hit_reward_scale(0.1f), hit_received_cost(0.3f), initial_nb_shells(10), nb_shells(initial_nb_shells), max_shells(30), fire_cooldown_frames(static_cast(1.f / 6.f / wanted_frame_frequency)), curr_cooldown_frame(fire_cooldown_frames), shells_recharged_per_hit(5), From b7c72f69d4060bc1e1280afe5d6b8a598f90995e Mon Sep 17 00:00:00 2001 From: samuel Date: Sat, 5 Sep 2026 12:58:31 +0200 Subject: [PATCH 03/57] * aim reward = 1.5 * (reward - baseline) * win reward only if tank has killed enemy * starving episode ending is more costly than death --- arenai_agent/src/core/train_environment.cpp | 2 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 16 ++++++++++------ arenai_model/src/tank/jolt_enemy_tank.h | 1 + .../tests/src/tests_reward/tests_reward.cpp | 4 ++-- 4 files changed, 14 insertions(+), 9 deletions(-) diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index 68436c91..d839ec1b 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -140,7 +140,7 @@ namespace arenai::agent { const auto &[state, reward, is_done] = step_result[winner_index]; - constexpr float win_reward = 2.f; + const float win_reward = nb_kills_per_tanks[winner_index] > 0 ? 2.f : 0.f; step_result[winner_index] = {state, reward + win_reward, true}; done[winner_index] = true; } diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 964243eb..4056d793 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -59,8 +59,8 @@ namespace arenai::model { }), max_frames_upside_down(static_cast(4.f / wanted_frame_frequency)), curr_frame_upside_down(0), miss_distance_scale(1.5f), miss_distance_exponent(1.f / 2.f), - hit_reward_scale(0.1f), hit_received_cost(0.3f), initial_nb_shells(10), - nb_shells(initial_nb_shells), max_shells(30), + hit_reward_scale(0.1f), aim_quality_baseline(0.5f), hit_received_cost(0.3f), + initial_nb_shells(10), nb_shells(initial_nb_shells), max_shells(30), fire_cooldown_frames(static_cast(1.f / 6.f / wanted_frame_frequency)), curr_cooldown_frame(fire_cooldown_frames), shells_recharged_per_hit(5), nb_frames_per_shell_regen(static_cast(1.5f / wanted_frame_frequency)), @@ -137,8 +137,9 @@ namespace arenai::model { float JoltEnemyTank::get_reward() const { RewardDetail detail; - // 1. dead / suicide penalty - detail.death = is_dead() ? -1.f : 0.f; + // 1. death penalty — starving out is the worst outcome: dying by hiding must cost + // more than dying in a fight (which already accumulates received-hit penalties) + detail.death = is_dead() ? (is_timeout() ? -2.f : -1.f) : 0.f; // 2. fired shells reward for (int i = static_cast(tracked_shells.size()) - 1; i >= 0; i--) { @@ -152,7 +153,9 @@ namespace arenai::model { const float aim_quality = compute_hit_reward( tracked.fire_pos, tracked.enemy_pos_at_t, tracked.shell_pos_at_t); - detail.aim += hit_reward_scale * aim_quality; + // centered on the baseline: a shell landing further than the tolerated + // miss costs, so firing is only subsidized when roughly on target + detail.aim += hit_reward_scale * (aim_quality - aim_quality_baseline); detail.nb_landed_shells++; detail.sum_aim_quality += aim_quality; @@ -369,8 +372,9 @@ namespace arenai::model { result.push_back(static_cast(nb_shells) / static_cast(max_shells)); result.push_back( static_cast(curr_cooldown_frame) / static_cast(fire_cooldown_frames)); + // hit/kill bonuses can push remaining_frames above the base window, hence the clamp result.push_back(std::clamp( - static_cast(remaining_frames) / static_cast(max_episode_frames), 0.f, + static_cast(remaining_frames) / static_cast(max_frames_without_hit), 0.f, 1.f)); return result; diff --git a/arenai_model/src/tank/jolt_enemy_tank.h b/arenai_model/src/tank/jolt_enemy_tank.h index 3abb9792..9c3a6bb9 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.h +++ b/arenai_model/src/tank/jolt_enemy_tank.h @@ -77,6 +77,7 @@ namespace arenai::model { float miss_distance_scale; float miss_distance_exponent; float hit_reward_scale; + float aim_quality_baseline; float hit_received_cost; diff --git a/arenai_model/tests/src/tests_reward/tests_reward.cpp b/arenai_model/tests/src/tests_reward/tests_reward.cpp index c72a2879..0f5f80f2 100644 --- a/arenai_model/tests/src/tests_reward/tests_reward.cpp +++ b/arenai_model/tests/src/tests_reward/tests_reward.cpp @@ -93,8 +93,8 @@ TEST_F(RewardTest, DeathPenaltyIsMinusOne) { const float death_reward = tanks[1]->get_reward(); // death and suicide share the same penalty so early termination is never an escape; - // the fatal hit also counts as a received hit (-0.15) - ASSERT_FLOAT_EQ(death_reward, -1.15f); + // the fatal hit also counts as a received hit (-0.3) + ASSERT_FLOAT_EQ(death_reward, -1.3f); } // ======================================================================== From 2bc422e50c9b68b843cdceeca934aa2dfc21ca85 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sat, 5 Sep 2026 13:01:10 +0200 Subject: [PATCH 04/57] curriculum learning : from 500m to 1000m (instead of 2000m) --- arenai_agent/src/main.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index 7d4eb989..b926c967 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -36,8 +36,8 @@ int main(const int argc, char **argv) { parser.add_argument("--vision_width").scan<'i', int>().default_value(256); parser.add_argument("--initial_spawn_width").scan<'g', float>().default_value(500.f); parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(500.f); - parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(2000.f); - parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(2000.f); + parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(1000.f); + parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(1000.f); parser.add_argument("--vision_num_threads") .scan<'i', int>() .default_value(static_cast(std::thread::hardware_concurrency())); From 36828a8ee4478cda83d3c041b870ebfd27015cae Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sat, 5 Sep 2026 22:50:27 +0200 Subject: [PATCH 05/57] no penalty on bad aim at impact --- arenai_model/src/tank/jolt_enemy_tank.cpp | 9 ++++----- arenai_model/src/tank/jolt_enemy_tank.h | 1 - 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 4056d793..33dcb1c2 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -59,8 +59,8 @@ namespace arenai::model { }), max_frames_upside_down(static_cast(4.f / wanted_frame_frequency)), curr_frame_upside_down(0), miss_distance_scale(1.5f), miss_distance_exponent(1.f / 2.f), - hit_reward_scale(0.1f), aim_quality_baseline(0.5f), hit_received_cost(0.3f), - initial_nb_shells(10), nb_shells(initial_nb_shells), max_shells(30), + hit_reward_scale(0.1f), hit_received_cost(0.3f), initial_nb_shells(10), + nb_shells(initial_nb_shells), max_shells(30), fire_cooldown_frames(static_cast(1.f / 6.f / wanted_frame_frequency)), curr_cooldown_frame(fire_cooldown_frames), shells_recharged_per_hit(5), nb_frames_per_shell_regen(static_cast(1.5f / wanted_frame_frequency)), @@ -153,9 +153,8 @@ namespace arenai::model { const float aim_quality = compute_hit_reward( tracked.fire_pos, tracked.enemy_pos_at_t, tracked.shell_pos_at_t); - // centered on the baseline: a shell landing further than the tolerated - // miss costs, so firing is only subsidized when roughly on target - detail.aim += hit_reward_scale * (aim_quality - aim_quality_baseline); + // pure bonus: firing must never be penalized, only rewarded when on target + detail.aim += hit_reward_scale * aim_quality; detail.nb_landed_shells++; detail.sum_aim_quality += aim_quality; diff --git a/arenai_model/src/tank/jolt_enemy_tank.h b/arenai_model/src/tank/jolt_enemy_tank.h index 9c3a6bb9..3abb9792 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.h +++ b/arenai_model/src/tank/jolt_enemy_tank.h @@ -77,7 +77,6 @@ namespace arenai::model { float miss_distance_scale; float miss_distance_exponent; float hit_reward_scale; - float aim_quality_baseline; float hit_received_cost; From 08571fb386a74ea676c4a524591fe3260593328c Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sun, 6 Sep 2026 00:57:01 +0200 Subject: [PATCH 06/57] curriculum learning based on hit ratio --- arenai_agent/src/core/spawn_curriculum.cpp | 57 ++++++++ arenai_agent/src/core/spawn_curriculum.h | 60 +++++++++ arenai_agent/src/core/train_environment.cpp | 13 +- arenai_agent/src/core/train_environment.h | 6 + arenai_agent/src/main.cpp | 10 ++ arenai_agent/src/train.cpp | 54 ++++++-- arenai_agent/src/train.h | 5 + .../tests_spawn_curriculum.cpp | 122 ++++++++++++++++++ 8 files changed, 316 insertions(+), 11 deletions(-) create mode 100644 arenai_agent/src/core/spawn_curriculum.cpp create mode 100644 arenai_agent/src/core/spawn_curriculum.h create mode 100644 arenai_agent/tests/src/tests_curriculum/tests_spawn_curriculum.cpp diff --git a/arenai_agent/src/core/spawn_curriculum.cpp b/arenai_agent/src/core/spawn_curriculum.cpp new file mode 100644 index 00000000..eb259cc2 --- /dev/null +++ b/arenai_agent/src/core/spawn_curriculum.cpp @@ -0,0 +1,57 @@ +// +// Created by samuel on 05/09/2026. +// + +#include "./spawn_curriculum.h" + +#include + +namespace arenai::agent { + + SpawnCurriculum::SpawnCurriculum( + const float delta_progress, const float ratio_low, const float ratio_high, + const int probe_window, const float boundary_proba, const std::uint64_t seed) + : upper(0.f), delta_progress(delta_progress), ratio_low(ratio_low), ratio_high(ratio_high), + probe_window(probe_window), boundary_proba(boundary_proba), last_was_probe(false), + nb_probe_episodes(0), sum_fires(0), sum_hits(0), rng(seed) {} + + float SpawnCurriculum::sample_progress() { + std::uniform_real_distribution unif(0.f, 1.f); + + last_was_probe = unif(rng) < boundary_proba; + if (last_was_probe) return upper; + + return upper * unif(rng); + } + + void SpawnCurriculum::on_episode_end(const int nb_fires, const int nb_hits) { + if (!last_was_probe) return; + + nb_probe_episodes++; + sum_fires += nb_fires; + sum_hits += nb_hits; + + if (nb_probe_episodes >= probe_window) attempt_update(); + } + + void SpawnCurriculum::attempt_update() { + // a window without a single fire counts as failure: an agent that stopped + // firing must not see its task keep hardening + const float ratio = + sum_fires > 0 ? static_cast(sum_hits) / static_cast(sum_fires) : 0.f; + + if (ratio > ratio_high) upper += delta_progress; + else if (ratio < ratio_low) upper -= delta_progress; + + upper = std::clamp(upper, 0.f, 1.f); + + nb_probe_episodes = 0; + sum_fires = 0; + sum_hits = 0; + } + + float SpawnCurriculum::upper_bound() const { return upper; } + + bool SpawnCurriculum::is_probe() const { return last_was_probe; } + +}// namespace arenai::agent diff --git a/arenai_agent/src/core/spawn_curriculum.h b/arenai_agent/src/core/spawn_curriculum.h new file mode 100644 index 00000000..ea0db398 --- /dev/null +++ b/arenai_agent/src/core/spawn_curriculum.h @@ -0,0 +1,60 @@ +// +// Created by samuel on 05/09/2026. +// + +#ifndef ARENAI_AGENT_HOST_SPAWN_CURRICULUM_H +#define ARENAI_AGENT_HOST_SPAWN_CURRICULUM_H + +#include +#include + +namespace arenai::agent { + + // ADR-style adaptive curriculum on the spawn-zone size. The difficulty is a + // progress fraction in [0, 1] mapped by the caller onto [initial, final] spawn + // sizes. Each episode either probes the current upper bound (probability + // boundary_proba) or draws uniformly below it — the mixture keeps easy episodes + // in the training data so long-range practice never erases short-range aim. + // Only probe episodes feed the controller: every probe_window of them, the + // aggregated hit/fire ratio moves the bound up past ratio_high, down below + // ratio_low, and not at all in between (hysteresis). + class SpawnCurriculum { + public: + SpawnCurriculum( + float delta_progress, float ratio_low, float ratio_high, int probe_window, + float boundary_proba, std::uint64_t seed); + + // draws the difficulty of the next episode, in [0, 1] + float sample_progress(); + + // per-episode totals across every tank; ignored unless the last sampled + // episode was a probe + void on_episode_end(int nb_fires, int nb_hits); + + float upper_bound() const; + + // whether the last sampled episode probes the upper bound + bool is_probe() const; + + private: + void attempt_update(); + + float upper; + + float delta_progress; + float ratio_low; + float ratio_high; + int probe_window; + float boundary_proba; + + bool last_was_probe; + int nb_probe_episodes; + long sum_fires; + long sum_hits; + + std::mt19937 rng; + }; + +}// namespace arenai::agent + +#endif// ARENAI_AGENT_HOST_SPAWN_CURRICULUM_H diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index d839ec1b..60606c0b 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -43,7 +43,8 @@ namespace arenai::agent { episode_step_mean_nb_metric(std::make_shared("s", 32, 1)), fire_metric(std::make_shared("fire", 256, 2)), hit_metric(std::make_shared("hit", 256, 2, true)), - kill_metric(std::make_shared("kill", 16, 1)), nb_kills_episode(0) {} + kill_metric(std::make_shared("kill", 16, 1)), nb_kills_episode(0), + nb_fires_episode(0), nb_hits_episode(0) {} std::vector> TrainTankEnvironment::step(const float time_delta, const std::vector &actions) { @@ -117,6 +118,9 @@ namespace arenai::agent { static_cast(nb_hits) / (static_cast(nb_acting) * wanted_frequency)); } + nb_fires_episode += nb_fires; + nb_hits_episode += nb_hits; + // step over tanks (hits and kills counters) for (int i = 0; i < step_result.size(); i++) { @@ -189,8 +193,15 @@ namespace arenai::agent { nb_hits_per_tanks = std::vector(nb_tanks, 0); nb_kills_per_tanks = std::vector(nb_tanks, 0); + + nb_fires_episode = 0; + nb_hits_episode = 0; } + int TrainTankEnvironment::episode_nb_fires() const { return nb_fires_episode; } + + int TrainTankEnvironment::episode_nb_hits() const { return nb_hits_episode; } + bool TrainTankEnvironment::are_all_done() { return std::accumulate( done.begin(), done.end(), true, diff --git a/arenai_agent/src/core/train_environment.h b/arenai_agent/src/core/train_environment.h index aa462064..f8779fcb 100644 --- a/arenai_agent/src/core/train_environment.h +++ b/arenai_agent/src/core/train_environment.h @@ -25,6 +25,10 @@ namespace arenai::agent { bool is_episode_terminated(); + // totals across every tank since the last reset, for the spawn curriculum + int episode_nb_fires() const; + int episode_nb_hits() const; + static void reset_singleton(); protected: @@ -67,6 +71,8 @@ namespace arenai::agent { std::shared_ptr kill_metric; int nb_kills_episode; + int nb_fires_episode; + int nb_hits_episode; bool are_all_done(); }; diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index b926c967..e9ff86cf 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -38,6 +38,11 @@ int main(const int argc, char **argv) { parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(500.f); parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(1000.f); parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(1000.f); + parser.add_argument("--curriculum_delta").scan<'g', float>().default_value(25.f); + parser.add_argument("--curriculum_ratio_low").scan<'g', float>().default_value(0.02f); + parser.add_argument("--curriculum_ratio_high").scan<'g', float>().default_value(0.04f); + parser.add_argument("--curriculum_probe_window").scan<'i', int>().default_value(24); + parser.add_argument("--curriculum_boundary_proba").scan<'g', float>().default_value(0.2f); parser.add_argument("--vision_num_threads") .scan<'i', int>() .default_value(static_cast(std::thread::hardware_concurrency())); @@ -73,6 +78,11 @@ int main(const int argc, char **argv) { .initial_spawn_height = parser.get("--initial_spawn_height"), .final_spawn_width = parser.get("--final_spawn_width"), .final_spawn_height = parser.get("--final_spawn_height"), + .curriculum_delta = parser.get("--curriculum_delta"), + .curriculum_ratio_low = parser.get("--curriculum_ratio_low"), + .curriculum_ratio_high = parser.get("--curriculum_ratio_high"), + .curriculum_probe_window = parser.get("--curriculum_probe_window"), + .curriculum_boundary_proba = parser.get("--curriculum_boundary_proba"), .num_threads = parser.get("--vision_num_threads")}, {.output_folder = std::filesystem::path(parser.get("--output_folder")), .resources_folder = std::filesystem::path(parser.get("--resources_folder")), diff --git a/arenai_agent/src/train.cpp b/arenai_agent/src/train.cpp index 19eb90df..ae364304 100644 --- a/arenai_agent/src/train.cpp +++ b/arenai_agent/src/train.cpp @@ -4,6 +4,8 @@ #include "./train.h" +#include +#include #include #include #include @@ -15,7 +17,9 @@ #include +#include "./core/spawn_curriculum.h" #include "./core/train_environment.h" +#include "./metrics/last_metric.h" #include "./metrics/metric_saver.h" #include "./networks_utils/torch_converter.h" #include "./networks_utils/torch_saver.h" @@ -49,6 +53,11 @@ namespace arenai::agent { {"initial_spawn_height", environment_options.initial_spawn_height}, {"final_spawn_width", environment_options.final_spawn_width}, {"final_spawn_height", environment_options.final_spawn_height}, + {"curriculum_delta", environment_options.curriculum_delta}, + {"curriculum_ratio_low", environment_options.curriculum_ratio_low}, + {"curriculum_ratio_high", environment_options.curriculum_ratio_high}, + {"curriculum_probe_window", environment_options.curriculum_probe_window}, + {"curriculum_boundary_proba", environment_options.curriculum_boundary_proba}, {"vision_num_threads", environment_options.num_threads}}}, {"agent", agent_config}}; @@ -84,15 +93,23 @@ namespace arenai::agent { train_options.max_episode_steps, environment_options.vision_height, environment_options.vision_width, environment_options.num_threads); - const float spawn_width_increase = - (environment_options.final_spawn_width - environment_options.initial_spawn_width) - / static_cast(train_options.nb_episodes); - const float spawn_height_increase = - (environment_options.final_spawn_height - environment_options.initial_spawn_height) - / static_cast(train_options.nb_episodes); + const float initial_spawn_side = std::sqrt( + environment_options.initial_spawn_width * environment_options.initial_spawn_height); + const float final_spawn_side = std::sqrt( + environment_options.final_spawn_width * environment_options.final_spawn_height); - float spawn_width = environment_options.initial_spawn_width; - float spawn_height = environment_options.initial_spawn_height; + // delta is given in meters on the spawn side; a non-growing range disables + // the curriculum (progress stays at 0, i.e. the initial spawn size) + const float curriculum_delta_progress = + final_spawn_side > initial_spawn_side + ? environment_options.curriculum_delta / (final_spawn_side - initial_spawn_side) + : 0.f; + + constexpr std::uint64_t curriculum_seed = 1337; + SpawnCurriculum curriculum( + curriculum_delta_progress, environment_options.curriculum_ratio_low, + environment_options.curriculum_ratio_high, environment_options.curriculum_probe_window, + environment_options.curriculum_boundary_proba, curriculum_seed); const auto agent = agent_factory->get_agent(); const auto collector = agent_factory->get_collector(); @@ -109,8 +126,14 @@ namespace arenai::agent { const auto sac_metrics = trainer->get_metrics(); const auto env_metrics = env->get_metrics(); + // curriculum observability: the side played this episode and the current bound + const auto spawn_side_metric = std::make_shared("side", 0); + const auto spawn_bound_metric = std::make_shared("D", 0); + std::vector> metrics; metrics.insert(metrics.end(), env_metrics.begin(), env_metrics.end()); + metrics.push_back(spawn_side_metric); + metrics.push_back(spawn_bound_metric); metrics.insert(metrics.end(), sac_metrics.begin(), sac_metrics.end()); MetricCsvSaver metric_csv_saver( @@ -139,8 +162,20 @@ namespace arenai::agent { int print_counter = 0; for (int episode_index = 0; episode_index < train_options.nb_episodes; episode_index++) { + const float progress = curriculum.sample_progress(); + + const float spawn_width = std::lerp( + environment_options.initial_spawn_width, environment_options.final_spawn_width, + progress); + const float spawn_height = std::lerp( + environment_options.initial_spawn_height, environment_options.final_spawn_height, + progress); const float spawn_side = std::sqrt(spawn_width * spawn_height); + spawn_side_metric->add(spawn_side); + spawn_bound_metric->add( + std::lerp(initial_spawn_side, final_spawn_side, curriculum.upper_bound())); + // set variable for episode bool is_done = false; @@ -200,8 +235,7 @@ namespace arenai::agent { env->stop_drawing(); - spawn_width += spawn_width_increase; - spawn_height += spawn_height_increase; + curriculum.on_episode_end(env->episode_nb_fires(), env->episode_nb_hits()); p_bar.tick(); } diff --git a/arenai_agent/src/train.h b/arenai_agent/src/train.h index 639f9f7d..18536bc7 100644 --- a/arenai_agent/src/train.h +++ b/arenai_agent/src/train.h @@ -30,6 +30,11 @@ namespace arenai::agent { float initial_spawn_height; float final_spawn_width; float final_spawn_height; + float curriculum_delta; + float curriculum_ratio_low; + float curriculum_ratio_high; + int curriculum_probe_window; + float curriculum_boundary_proba; int num_threads; }; diff --git a/arenai_agent/tests/src/tests_curriculum/tests_spawn_curriculum.cpp b/arenai_agent/tests/src/tests_curriculum/tests_spawn_curriculum.cpp new file mode 100644 index 00000000..b8e17b86 --- /dev/null +++ b/arenai_agent/tests/src/tests_curriculum/tests_spawn_curriculum.cpp @@ -0,0 +1,122 @@ +// +// Created by samuel on 05/09/2026. +// + +#include +#include + +using namespace arenai; +using namespace arenai::agent; + +namespace { + constexpr float DELTA = 0.05f; + constexpr float RATIO_LOW = 0.02f; + constexpr float RATIO_HIGH = 0.04f; + constexpr int PROBE_WINDOW = 4; + constexpr std::uint64_t SEED = 42; + + // feed exactly one controller window of probe episodes with the given ratio + void run_probe_window(SpawnCurriculum &curriculum, const int nb_fires, const int nb_hits) { + int done = 0; + while (done < PROBE_WINDOW) { + curriculum.sample_progress(); + const bool is_probe = curriculum.is_probe(); + + curriculum.on_episode_end(nb_fires, nb_hits); + if (is_probe) done++; + } + } +}// namespace + +TEST(SpawnCurriculumTest, StartsAtZero) { + const SpawnCurriculum curriculum(DELTA, RATIO_LOW, RATIO_HIGH, PROBE_WINDOW, 0.2f, SEED); + + ASSERT_FLOAT_EQ(curriculum.upper_bound(), 0.f); +} + +TEST(SpawnCurriculumTest, SampleStaysWithinBound) { + SpawnCurriculum curriculum(DELTA, RATIO_LOW, RATIO_HIGH, PROBE_WINDOW, 0.2f, SEED); + + run_probe_window(curriculum, 100, 10);// grow once so the bound is not 0 + + for (int i = 0; i < 1000; i++) { + const float upper = curriculum.upper_bound(); + const float progress = curriculum.sample_progress(); + ASSERT_GE(progress, 0.f); + ASSERT_LE(progress, upper); + } +} + +TEST(SpawnCurriculumTest, GrowsAboveHighRatio) { + SpawnCurriculum curriculum(DELTA, RATIO_LOW, RATIO_HIGH, PROBE_WINDOW, 0.2f, SEED); + + run_probe_window(curriculum, 100, 10);// ratio 0.1 > high + + ASSERT_FLOAT_EQ(curriculum.upper_bound(), DELTA); +} + +TEST(SpawnCurriculumTest, ShrinksBelowLowRatio) { + SpawnCurriculum curriculum(DELTA, RATIO_LOW, RATIO_HIGH, PROBE_WINDOW, 0.2f, SEED); + + run_probe_window(curriculum, 100, 10); + run_probe_window(curriculum, 100, 10); + ASSERT_FLOAT_EQ(curriculum.upper_bound(), 2.f * DELTA); + + run_probe_window(curriculum, 100, 1);// ratio 0.01 < low + + ASSERT_FLOAT_EQ(curriculum.upper_bound(), DELTA); +} + +TEST(SpawnCurriculumTest, HoldsInsideHysteresisBand) { + SpawnCurriculum curriculum(DELTA, RATIO_LOW, RATIO_HIGH, PROBE_WINDOW, 0.2f, SEED); + + run_probe_window(curriculum, 100, 10); + ASSERT_FLOAT_EQ(curriculum.upper_bound(), DELTA); + + run_probe_window(curriculum, 100, 3);// ratio 0.03, between low and high + + ASSERT_FLOAT_EQ(curriculum.upper_bound(), DELTA); +} + +TEST(SpawnCurriculumTest, NoFireCountsAsFailure) { + SpawnCurriculum curriculum(DELTA, RATIO_LOW, RATIO_HIGH, PROBE_WINDOW, 0.2f, SEED); + + run_probe_window(curriculum, 100, 10); + ASSERT_FLOAT_EQ(curriculum.upper_bound(), DELTA); + + run_probe_window(curriculum, 0, 0); + + ASSERT_FLOAT_EQ(curriculum.upper_bound(), 0.f); +} + +TEST(SpawnCurriculumTest, ClampsToUnitInterval) { + SpawnCurriculum curriculum(DELTA, RATIO_LOW, RATIO_HIGH, PROBE_WINDOW, 0.2f, SEED); + + // more than enough successful windows to reach the top + for (int i = 0; i < 30; i++) run_probe_window(curriculum, 100, 10); + ASSERT_FLOAT_EQ(curriculum.upper_bound(), 1.f); + + // and enough failures to reach the bottom + for (int i = 0; i < 30; i++) run_probe_window(curriculum, 100, 0); + ASSERT_FLOAT_EQ(curriculum.upper_bound(), 0.f); +} + +TEST(SpawnCurriculumTest, NonProbeEpisodesDoNotFeedController) { + // boundary_proba 0: no episode is a probe, the controller must never move + // whatever the results + SpawnCurriculum curriculum(DELTA, RATIO_LOW, RATIO_HIGH, PROBE_WINDOW, 0.f, SEED); + for (int i = 0; i < 100; i++) { + curriculum.sample_progress(); + curriculum.on_episode_end(100, 10); + } + + ASSERT_FLOAT_EQ(curriculum.upper_bound(), 0.f); +} + +TEST(SpawnCurriculumTest, ZeroDeltaKeepsBoundFixed) { + SpawnCurriculum curriculum(0.f, RATIO_LOW, RATIO_HIGH, PROBE_WINDOW, 0.2f, SEED); + + for (int i = 0; i < 10; i++) run_probe_window(curriculum, 100, 10); + + ASSERT_FLOAT_EQ(curriculum.upper_bound(), 0.f); +} From 767f74f8416cac2342f29daea8f4fd6e0bf01f11 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 6 Sep 2026 15:37:43 +0200 Subject: [PATCH 07/57] start recurrent --- arenai_agent/src/core/spawn_curriculum.cpp | 2 +- arenai_agent/src/main.cpp | 4 +- arenai_agent/src/networks/recurrent/actor.h | 8 + arenai_agent/src/networks/recurrent/critic.h | 8 + .../networks/recurrent/liquid_recurrent.cpp | 102 +++++++++++ .../src/networks/recurrent/liquid_recurrent.h | 65 +++++++ .../tests_networks/tests_liquid_recurrent.h | 27 +++ .../tests_networks/tests_liquid_recurrent.cpp | 167 ++++++++++++++++++ 8 files changed, 380 insertions(+), 3 deletions(-) create mode 100644 arenai_agent/src/networks/recurrent/actor.h create mode 100644 arenai_agent/src/networks/recurrent/critic.h create mode 100644 arenai_agent/src/networks/recurrent/liquid_recurrent.cpp create mode 100644 arenai_agent/src/networks/recurrent/liquid_recurrent.h create mode 100644 arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_liquid_recurrent.h create mode 100644 arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp diff --git a/arenai_agent/src/core/spawn_curriculum.cpp b/arenai_agent/src/core/spawn_curriculum.cpp index eb259cc2..3a28d12a 100644 --- a/arenai_agent/src/core/spawn_curriculum.cpp +++ b/arenai_agent/src/core/spawn_curriculum.cpp @@ -16,7 +16,7 @@ namespace arenai::agent { nb_probe_episodes(0), sum_fires(0), sum_hits(0), rng(seed) {} float SpawnCurriculum::sample_progress() { - std::uniform_real_distribution unif(0.f, 1.f); + std::uniform_real_distribution unif(0.f, 1.f); last_was_probe = unif(rng) < boundary_proba; if (last_was_probe) return upper; diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index e9ff86cf..10aff1c4 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -34,8 +34,8 @@ int main(const int argc, char **argv) { parser.add_argument("--nb_tanks").scan<'i', int>().default_value(32); parser.add_argument("--vision_height").scan<'i', int>().default_value(128); parser.add_argument("--vision_width").scan<'i', int>().default_value(256); - parser.add_argument("--initial_spawn_width").scan<'g', float>().default_value(500.f); - parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(500.f); + parser.add_argument("--initial_spawn_width").scan<'g', float>().default_value(250.f); + parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(250.f); parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(1000.f); parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(1000.f); parser.add_argument("--curriculum_delta").scan<'g', float>().default_value(25.f); diff --git a/arenai_agent/src/networks/recurrent/actor.h b/arenai_agent/src/networks/recurrent/actor.h new file mode 100644 index 00000000..03f65b27 --- /dev/null +++ b/arenai_agent/src/networks/recurrent/actor.h @@ -0,0 +1,8 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_ACTOR_H +#define ARENAI_ACTOR_H + +#endif//ARENAI_ACTOR_H diff --git a/arenai_agent/src/networks/recurrent/critic.h b/arenai_agent/src/networks/recurrent/critic.h new file mode 100644 index 00000000..6877171d --- /dev/null +++ b/arenai_agent/src/networks/recurrent/critic.h @@ -0,0 +1,8 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_CRITIC_H +#define ARENAI_CRITIC_H + +#endif//ARENAI_CRITIC_H diff --git a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp b/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp new file mode 100644 index 00000000..c44a5e8a --- /dev/null +++ b/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp @@ -0,0 +1,102 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./liquid_recurrent.h" + +/* + * Cell model + */ + +CellModel::CellModel( + const int neuron_number, const int input_size, + const std::function &activation_function) + : weights(register_module( + "weights", + torch::nn::Linear(torch::nn::LinearOptions(input_size, neuron_number).bias(false)))), + recurrent_weights(register_module( + "recurrent_weights", + torch::nn::Linear(torch::nn::LinearOptions(neuron_number, neuron_number).bias(false)))), + biases(register_parameter("biases", torch::zeros({1, neuron_number}))), + activation_function(activation_function) { + // TODO init here +} + +torch::Tensor CellModel::forward(const torch::Tensor &x_t, const torch::Tensor &input_t) { + return activation_function(recurrent_weights(x_t) + weights(input_t) + biases); +} + +/* + * Liquid cell + */ +LiquidCell::LiquidCell( + const int neuron_number, const int input_size, const int unfolding_steps, + const std::function &activation_function, + const float delta_t) + : a(register_parameter("a", torch::ones({1, neuron_number}))), + raw_tau(register_parameter("raw_tau", torch::zeros({1, neuron_number}))), + f(register_module( + "f", std::make_shared(neuron_number, input_size, activation_function))), + unfolding_steps(unfolding_steps), delta_t(delta_t) {} + +torch::Tensor LiquidCell::forward(const torch::Tensor &x_t, const torch::Tensor &input_t) { + auto x_t_next = x_t; + const auto curr_delta_t = delta_t / static_cast(unfolding_steps); + + for (int i = 0; i < unfolding_steps; ++i) { + const auto f_output = f->forward(x_t_next, input_t); + x_t_next = (x_t_next + curr_delta_t * f_output * a) + / (1.0 + curr_delta_t * (1.0 / tau() + f_output)); + } + + return x_t_next; +} + +torch::Tensor LiquidCell::tau() const { return torch::nn::functional::softplus(raw_tau) + 1e-3; } + +/* + * Liquid recurrent + */ + +LiquidRecurrent::LiquidRecurrent( + int neuron_number, int input_size, int output_size, int unfolding_steps, + const std::function &activation_function, float delta_t) + : cell(register_module( + "cell", std::make_shared( + neuron_number, input_size, unfolding_steps, activation_function, delta_t))), + neuron_number(neuron_number), + to_output(register_module( + "to_output", + torch::nn::Sequential( + torch::nn::Linear(torch::nn::LinearOptions(neuron_number, output_size).bias(false)), + torch::nn::LayerNorm( + torch::nn::LayerNormOptions({output_size}).elementwise_affine(true)), + torch::nn::SiLU()))) {} + +torch::Tensor LiquidRecurrent::forward(const torch::Tensor &inputs) { + TORCH_CHECK( + inputs.sizes().size() == 3, + "Processed input needs to have 3 dimensions (Batch, Time, Features)"); + + const long batch_size = inputs.size(0); + const long nb_steps = inputs.size(1); + + auto x_t = get_first_x(static_cast(batch_size)); + + std::vector results; + results.reserve(nb_steps); + + for (long t = 0; t < nb_steps; t++) { + x_t = cell->forward(x_t, inputs.index({at::indexing::Slice(), t, at::indexing::Slice()})); + + const auto output = to_output->forward(x_t); + results.push_back(output); + } + + // (batch, time, output_features) + return torch::stack(results, 1); +} + +torch::Tensor LiquidRecurrent::get_first_x(int batch_size) { + return 1e-1 * torch::randn({batch_size, neuron_number}, parameters().back().device()); +} diff --git a/arenai_agent/src/networks/recurrent/liquid_recurrent.h b/arenai_agent/src/networks/recurrent/liquid_recurrent.h new file mode 100644 index 00000000..67489243 --- /dev/null +++ b/arenai_agent/src/networks/recurrent/liquid_recurrent.h @@ -0,0 +1,65 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_LIQUID_RECURRENT_H +#define ARENAI_LIQUID_RECURRENT_H + +#include + +class CellModel : public torch::nn::Module { +public: + CellModel( + int neuron_number, int input_size, + const std::function &activation_function); + + torch::Tensor forward(const torch::Tensor &x_t, const torch::Tensor &input_t); + +private: + torch::nn::Linear weights; + torch::nn::Linear recurrent_weights; + torch::Tensor biases; + + std::function activation_function; +}; + +class LiquidCell : public torch::nn::Module { +public: + LiquidCell( + int neuron_number, int input_size, int unfolding_steps, + const std::function &activation_function, + float delta_t); + + torch::Tensor forward(const torch::Tensor &x_t, const torch::Tensor &input_t); + +private: + torch::Tensor a; + torch::Tensor raw_tau; + + std::shared_ptr f; + + int unfolding_steps; + float delta_t; + + torch::Tensor tau() const; +}; + +class LiquidRecurrent : public torch::nn::Module { +public: + LiquidRecurrent( + int neuron_number, int input_size, int output_size, int unfolding_steps, + const std::function &activation_function, + float delta_t); + + torch::Tensor forward(const torch::Tensor &inputs); + +private: + std::shared_ptr cell; + int neuron_number; + + torch::nn::Sequential to_output; + + torch::Tensor get_first_x(int batch_size); +}; + +#endif//ARENAI_LIQUID_RECURRENT_H diff --git a/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_liquid_recurrent.h b/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_liquid_recurrent.h new file mode 100644 index 00000000..45fbc427 --- /dev/null +++ b/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_liquid_recurrent.h @@ -0,0 +1,27 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_TESTS_LIQUID_RECURRENT_H +#define ARENAI_TESTS_LIQUID_RECURRENT_H + +#include + +typedef int NeuronNumber; +typedef int InputSize; +typedef int OutputSize; +typedef int UnfoldingSteps; +typedef int BatchSize; +typedef int TimeSteps; + +class CellModelTestParam + : public testing::TestWithParam> {}; + +class LiquidCellTestParam : public testing::TestWithParam< + std::tuple> {}; + +class LiquidRecurrentTestParam + : public testing::TestWithParam< + std::tuple> {}; + +#endif//ARENAI_TESTS_LIQUID_RECURRENT_H diff --git a/arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp b/arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp new file mode 100644 index 00000000..17e4cc49 --- /dev/null +++ b/arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp @@ -0,0 +1,167 @@ +// +// Created by samuel on 06/09/2026. +// + +#include + +#include + +namespace { + constexpr float DELTA_T = 1.f; + + torch::Tensor tanh_activation(const torch::Tensor &t) { return torch::tanh(t); } + + void assert_all_parameters_receive_gradient(const torch::nn::Module &module) { + for (const auto &p: module.named_parameters()) { + ASSERT_TRUE(p.value().grad().defined()) + << "Parameter \"" << p.key() << "\" has no gradient"; + ASSERT_TRUE(torch::all(torch::isfinite(p.value().grad())).item()) + << "Parameter \"" << p.key() << "\" has non-finite gradients"; + ASSERT_GT(p.value().grad().abs().sum().item(), 0.f) + << "Parameter \"" << p.key() << "\" has an all-zero gradient"; + } + } +}// namespace + +/* + * Cell model + */ + +TEST_P(CellModelTestParam, OutputShape) { + const auto [neuron_number, input_size, batch_size] = GetParam(); + + CellModel cell_model(neuron_number, input_size, tanh_activation); + + const auto x_t = torch::randn({batch_size, neuron_number}); + const auto input_t = torch::randn({batch_size, input_size}); + + const auto output = cell_model.forward(x_t, input_t); + + ASSERT_EQ(output.ndimension(), 2); + ASSERT_EQ(output.size(0), batch_size); + ASSERT_EQ(output.size(1), neuron_number); +} + +TEST_P(CellModelTestParam, GradientFlows) { + const auto [neuron_number, input_size, batch_size] = GetParam(); + + CellModel cell_model(neuron_number, input_size, tanh_activation); + + const auto x_t = torch::randn({batch_size, neuron_number}, torch::requires_grad()); + const auto input_t = torch::randn({batch_size, input_size}, torch::requires_grad()); + + cell_model.forward(x_t, input_t).sum().backward(); + + assert_all_parameters_receive_gradient(cell_model); + + ASSERT_TRUE(x_t.grad().defined()); + ASSERT_GT(x_t.grad().abs().sum().item(), 0.f); + ASSERT_TRUE(input_t.grad().defined()); + ASSERT_GT(input_t.grad().abs().sum().item(), 0.f); +} + +INSTANTIATE_TEST_SUITE_P( + TestCellModel, CellModelTestParam, + testing::Combine(testing::Values(2, 8, 16), testing::Values(1, 3, 12), testing::Values(1, 4))); + +/* + * Liquid cell + */ + +TEST_P(LiquidCellTestParam, OutputShape) { + const auto [neuron_number, input_size, unfolding_steps, batch_size] = GetParam(); + + LiquidCell liquid_cell(neuron_number, input_size, unfolding_steps, tanh_activation, DELTA_T); + + const auto x_t = torch::randn({batch_size, neuron_number}); + const auto input_t = torch::randn({batch_size, input_size}); + + const auto output = liquid_cell.forward(x_t, input_t); + + ASSERT_EQ(output.ndimension(), 2); + ASSERT_EQ(output.size(0), batch_size); + ASSERT_EQ(output.size(1), neuron_number); +} + +TEST_P(LiquidCellTestParam, GradientFlows) { + const auto [neuron_number, input_size, unfolding_steps, batch_size] = GetParam(); + + LiquidCell liquid_cell(neuron_number, input_size, unfolding_steps, tanh_activation, DELTA_T); + + const auto x_t = torch::randn({batch_size, neuron_number}, torch::requires_grad()); + const auto input_t = torch::randn({batch_size, input_size}, torch::requires_grad()); + + liquid_cell.forward(x_t, input_t).sum().backward(); + + assert_all_parameters_receive_gradient(liquid_cell); + + ASSERT_TRUE(x_t.grad().defined()); + ASSERT_GT(x_t.grad().abs().sum().item(), 0.f); + ASSERT_TRUE(input_t.grad().defined()); + ASSERT_GT(input_t.grad().abs().sum().item(), 0.f); +} + +INSTANTIATE_TEST_SUITE_P( + TestLiquidCell, LiquidCellTestParam, + testing::Combine( + testing::Values(2, 8, 16), testing::Values(1, 3, 12), testing::Values(1, 2, 6), + testing::Values(1, 4))); + +/* + * Liquid recurrent + */ + +TEST_P(LiquidRecurrentTestParam, OutputShape) { + const auto [neuron_number, input_size, output_size, unfolding_steps, batch_size, time_steps] = + GetParam(); + + LiquidRecurrent liquid_recurrent( + neuron_number, input_size, output_size, unfolding_steps, tanh_activation, DELTA_T); + + const auto inputs = torch::randn({batch_size, time_steps, input_size}); + + const auto output = liquid_recurrent.forward(inputs); + + ASSERT_EQ(output.ndimension(), 3); + ASSERT_EQ(output.size(0), batch_size); + ASSERT_EQ(output.size(1), time_steps); + ASSERT_EQ(output.size(2), output_size); +} + +TEST_P(LiquidRecurrentTestParam, GradientFlows) { + const auto [neuron_number, input_size, output_size, unfolding_steps, batch_size, time_steps] = + GetParam(); + + // LayerNorm over a single feature outputs exactly zero (x - mean(x) == 0), + // which blocks any gradient from reaching the layers before it + if (output_size == 1) GTEST_SKIP() << "LayerNorm({1}) zeroes the signal, no gradient flows"; + + LiquidRecurrent liquid_recurrent( + neuron_number, input_size, output_size, unfolding_steps, tanh_activation, DELTA_T); + + const auto inputs = torch::randn({batch_size, time_steps, input_size}, torch::requires_grad()); + + liquid_recurrent.forward(inputs).sum().backward(); + + assert_all_parameters_receive_gradient(liquid_recurrent); + + ASSERT_TRUE(inputs.grad().defined()); + ASSERT_TRUE(torch::all(torch::isfinite(inputs.grad())).item()); + // every time step must contribute to the loss + const auto per_step_grad = inputs.grad().abs().sum(std::vector{0, 2}); + ASSERT_TRUE(torch::all(torch::gt(per_step_grad, 0.f)).item()) + << "Some time steps received no gradient: " << per_step_grad; +} + +TEST(LiquidRecurrentEdge, RejectsNon3DInput) { + LiquidRecurrent liquid_recurrent(4, 3, 2, 2, tanh_activation, DELTA_T); + + EXPECT_THROW(liquid_recurrent.forward(torch::randn({2, 3})), c10::Error); + EXPECT_THROW(liquid_recurrent.forward(torch::randn({2, 5, 3, 1})), c10::Error); +} + +INSTANTIATE_TEST_SUITE_P( + TestLiquidRecurrent, LiquidRecurrentTestParam, + testing::Combine( + testing::Values(2, 8, 16), testing::Values(1, 3, 12), testing::Values(1, 2, 6), + testing::Values(1, 3), testing::Values(1, 4), testing::Values(1, 2, 5))); From a3486d2e23a5ab36e70640a9750e54d1f75c2d60 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 6 Sep 2026 16:09:12 +0200 Subject: [PATCH 08/57] add liquid ppo --- arenai_agent/src/agents/agent_cli.cpp | 4 + .../agents/ppo_liquid/liquid_hidden_state.cpp | 25 + .../agents/ppo_liquid/liquid_hidden_state.h | 38 ++ .../agents/ppo_liquid/liquid_ppo_agent.cpp | 78 ++++ .../src/agents/ppo_liquid/liquid_ppo_agent.h | 39 ++ .../ppo_liquid/liquid_ppo_collector.cpp | 51 +++ .../agents/ppo_liquid/liquid_ppo_collector.h | 49 ++ .../agents/ppo_liquid/liquid_ppo_factory.cpp | 48 ++ .../agents/ppo_liquid/liquid_ppo_factory.h | 45 ++ .../ppo_liquid/liquid_ppo_hyperparams.cpp | 39 ++ .../ppo_liquid/liquid_ppo_hyperparams.h | 48 ++ .../ppo_liquid/liquid_ppo_rollout_buffer.cpp | 108 +++++ .../ppo_liquid/liquid_ppo_rollout_buffer.h | 77 ++++ .../agents/ppo_liquid/liquid_ppo_trainer.cpp | 430 ++++++++++++++++++ .../agents/ppo_liquid/liquid_ppo_trainer.h | 121 +++++ arenai_agent/src/networks/recurrent/actor.h | 8 - arenai_agent/src/networks/recurrent/critic.h | 8 - .../src/networks/recurrent/liquid_actor.cpp | 110 +++++ .../src/networks/recurrent/liquid_actor.h | 65 +++ .../src/networks/recurrent/liquid_critic.cpp | 81 ++++ .../src/networks/recurrent/liquid_critic.h | 58 +++ .../networks/recurrent/liquid_recurrent.cpp | 11 +- .../src/networks/recurrent/liquid_recurrent.h | 8 +- .../tests_agents/tests_liquid_ppo.h | 44 ++ .../tests_liquid_ppo_rollout_buffer.h | 28 ++ .../tests_agents/tests_liquid_ppo_training.h | 41 ++ .../src/tests_agents/tests_liquid_ppo.cpp | 195 ++++++++ .../tests_liquid_ppo_rollout_buffer.cpp | 237 ++++++++++ .../tests_liquid_ppo_training.cpp | 179 ++++++++ 29 files changed, 2253 insertions(+), 20 deletions(-) create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_hidden_state.cpp create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_hidden_state.h create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.h create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.cpp create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.h create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_factory.cpp create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_factory.h create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.cpp create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.cpp create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.h create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp create mode 100644 arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.h delete mode 100644 arenai_agent/src/networks/recurrent/actor.h delete mode 100644 arenai_agent/src/networks/recurrent/critic.h create mode 100644 arenai_agent/src/networks/recurrent/liquid_actor.cpp create mode 100644 arenai_agent/src/networks/recurrent/liquid_actor.h create mode 100644 arenai_agent/src/networks/recurrent/liquid_critic.cpp create mode 100644 arenai_agent/src/networks/recurrent/liquid_critic.h create mode 100644 arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo.h create mode 100644 arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo_rollout_buffer.h create mode 100644 arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo_training.h create mode 100644 arenai_agent/tests/src/tests_agents/tests_liquid_ppo.cpp create mode 100644 arenai_agent/tests/src/tests_agents/tests_liquid_ppo_rollout_buffer.cpp create mode 100644 arenai_agent/tests/src/tests_agents/tests_liquid_ppo_training.cpp diff --git a/arenai_agent/src/agents/agent_cli.cpp b/arenai_agent/src/agents/agent_cli.cpp index 7e0d671b..9242121c 100644 --- a/arenai_agent/src/agents/agent_cli.cpp +++ b/arenai_agent/src/agents/agent_cli.cpp @@ -9,6 +9,8 @@ #include "../utils/cli_fields.h" #include "./ppo/ppo_factory.h" #include "./ppo/ppo_hyperparams.h" +#include "./ppo_liquid/liquid_ppo_factory.h" +#include "./ppo_liquid/liquid_ppo_hyperparams.h" #include "./sac/sac_factory.h" #include "./sac/sac_hyperparams.h" @@ -43,6 +45,8 @@ namespace arenai::agent { make_agent_cli("sac", sac_cli_fields())); algorithms.push_back( make_agent_cli("ppo", ppo_cli_fields())); + algorithms.push_back(make_agent_cli( + "ppo_liquid", liquid_ppo_cli_fields())); return algorithms; } diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_hidden_state.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_hidden_state.cpp new file mode 100644 index 00000000..9befd04c --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_hidden_state.cpp @@ -0,0 +1,25 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./liquid_hidden_state.h" + +using namespace arenai; +using namespace arenai::agent; + +namespace arenai::agent { + + LiquidHiddenState::LiquidHiddenState(std::shared_ptr actor) + : actor(std::move(actor)) {} + + torch::Tensor LiquidHiddenState::get(const long batch_size) { + if (!x.defined() || x.size(0) != batch_size) + x = actor->initial_state(static_cast(batch_size)); + return x; + } + + void LiquidHiddenState::set(const torch::Tensor &next_x) { x = next_x.detach(); } + + void LiquidHiddenState::reset() { x = torch::Tensor(); } + +}// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_hidden_state.h b/arenai_agent/src/agents/ppo_liquid/liquid_hidden_state.h new file mode 100644 index 00000000..51a8e904 --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_hidden_state.h @@ -0,0 +1,38 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_LIQUID_HIDDEN_STATE_H +#define ARENAI_LIQUID_HIDDEN_STATE_H + +#include + +#include + +#include "../../networks/recurrent/liquid_actor.h" + +namespace arenai::agent { + + // Actor liquid state carried across act() calls, shared between the agent + // (reads it before each step, advances it after) and the collector (resets + // it when the episode ends). + class LiquidHiddenState { + public: + explicit LiquidHiddenState(std::shared_ptr actor); + + // the state opening the next step; re-drawn on first use, on a batch + // size change and after reset() + torch::Tensor get(long batch_size); + void set(const torch::Tensor &next_x); + + // fresh episode: the next get() re-draws every tank's initial state + void reset(); + + private: + std::shared_ptr actor; + torch::Tensor x; + }; + +}// namespace arenai::agent + +#endif//ARENAI_LIQUID_HIDDEN_STATE_H diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp new file mode 100644 index 00000000..5e685b3d --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp @@ -0,0 +1,78 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./liquid_ppo_agent.h" + +#include "../../distributions/multinomial.h" +#include "../../distributions/truncated_normal.h" +#include "../../networks/constants.h" +#include "../../networks_utils/torch_converter.h" +#include "../../networks_utils/torch_loader.h" + +using namespace arenai; +using namespace arenai::agent; + +namespace arenai::agent { + + /* + * Torch liquid PPO agent + */ + + TorchLiquidPpoAgent::TorchLiquidPpoAgent( + const std::shared_ptr &actor, + const std::shared_ptr &hidden_state, const torch::Device device, + std::optional> collector) + : actor(actor), hidden_state(hidden_state), collector(std::move(collector)) { + actor->to(device); + } + + std::vector TorchLiquidPpoAgent::act( + const std::vector &states, const int vision_height, const int vision_width) { + const auto [continuous_action, discrete_action] = + act(states_to_tensor(states, vision_height, vision_width), false); + return tensor_to_actions(continuous_action, discrete_action); + } + + TorchAction TorchLiquidPpoAgent::act(const TorchState &state, const bool sample) { + TorchAction action; + torch::Tensor continuous_log_prob; + torch::Tensor discrete_log_prob; + torch::Tensor x_t; + + { + torch::NoGradGuard guard; + + const auto &[vision, sensors] = state; + + x_t = hidden_state->get(vision.size(0)); + const auto &[mu, sigma, discrete_proba, next_x] = actor->act(vision, sensors, x_t); + hidden_state->set(next_x); + + if (sample) { + action.continuous_action = truncated_normal_sample(mu, sigma); + action.discrete_action = multinomial_sample(discrete_proba); + } else { + action.continuous_action = truncated_normal_mean(mu, sigma); + action.discrete_action = multinomial_max_action(discrete_proba); + } + + // old log-probabilities, kept for the PPO importance ratio + continuous_log_prob = + truncated_normal_log_pdf(action.continuous_action, mu, sigma).sum(-1, true); + + const auto clamped_proba = torch::clamp(discrete_proba, EPSILON, 1.0 - EPSILON); + discrete_log_prob = (action.discrete_action * torch::log(clamped_proba)).sum(-1, true); + } + + if (collector.has_value()) + collector.value()->on_act(state, action, continuous_log_prob, discrete_log_prob, x_t); + + return action; + } + + void TorchLiquidPpoAgent::load(const std::filesystem::path &agent_folder) { + load_torch(agent_folder, actor, "actor.pt"); + } + +}// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.h b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.h new file mode 100644 index 00000000..bace4608 --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.h @@ -0,0 +1,39 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_LIQUID_PPO_AGENT_H +#define ARENAI_LIQUID_PPO_AGENT_H + +#include + +#include "../../networks/recurrent/liquid_actor.h" +#include "../torch_agent.h" +#include "./liquid_hidden_state.h" +#include "./liquid_ppo_collector.h" + +namespace arenai::agent { + + class TorchLiquidPpoAgent final : public virtual AbstractAgent, + public virtual AbstractTorchAgent { + public: + TorchLiquidPpoAgent( + const std::shared_ptr &actor, + const std::shared_ptr &hidden_state, torch::Device device, + std::optional> collector = std::nullopt); + + TorchAction act(const TorchState &state, bool sample) override; + + std::vector + act(const std::vector &states, int vision_height, int vision_width) override; + void load(const std::filesystem::path &agent_folder) override; + + private: + std::shared_ptr actor; + std::shared_ptr hidden_state; + std::optional> collector; + }; + +}// namespace arenai::agent + +#endif//ARENAI_LIQUID_PPO_AGENT_H diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.cpp new file mode 100644 index 00000000..8fc8eefd --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.cpp @@ -0,0 +1,51 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./liquid_ppo_collector.h" + +using namespace arenai; +using namespace arenai::agent; + +namespace arenai::agent { + + LiquidPpoStepCollector::LiquidPpoStepCollector( + std::shared_ptr rollout_buffer, + std::shared_ptr hidden_state) + : rollout_buffer(std::move(rollout_buffer)), hidden_state(std::move(hidden_state)) {} + + void LiquidPpoStepCollector::on_act( + const TorchState &state, const TorchAction &action, + const torch::Tensor &continuous_log_prob, const torch::Tensor &discrete_log_prob, + const torch::Tensor &actor_hidden) { + last_state = state; + last_action = action; + last_continuous_log_prob = continuous_log_prob; + last_discrete_log_prob = discrete_log_prob; + last_actor_hidden = actor_hidden; + } + + void + LiquidPpoStepCollector::on_transition(const torch::Tensor &rewards, const torch::Tensor &done) { + rollout_buffer->add( + {.state = last_state, + .action = last_action, + .continuous_log_prob = last_continuous_log_prob, + .discrete_log_prob = last_discrete_log_prob, + .reward = rewards, + .done = done, + .actor_hidden = last_actor_hidden, + .episode_start = next_episode_start}); + + next_episode_start = false; + } + + void LiquidPpoStepCollector::on_episode_end(const TorchState &final_state) { + rollout_buffer->finish_episode(final_state); + + // the environment resets every tank: re-draw the liquid states + hidden_state->reset(); + next_episode_start = true; + } + +}// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.h b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.h new file mode 100644 index 00000000..155735db --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.h @@ -0,0 +1,49 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_LIQUID_PPO_COLLECTOR_H +#define ARENAI_LIQUID_PPO_COLLECTOR_H + +#include + +#include "../step_collector.h" +#include "../torch_types.h" +#include "./liquid_hidden_state.h" +#include "./liquid_ppo_rollout_buffer.h" + +namespace arenai::agent { + + class LiquidPpoStepCollector final : public AbstractStepCollector { + public: + LiquidPpoStepCollector( + std::shared_ptr rollout_buffer, + std::shared_ptr hidden_state); + + // concrete act-time channel, called by TorchLiquidPpoAgent::act + void on_act( + const TorchState &state, const TorchAction &action, + const torch::Tensor &continuous_log_prob, const torch::Tensor &discrete_log_prob, + const torch::Tensor &actor_hidden); + + void on_transition(const torch::Tensor &rewards, const torch::Tensor &done) override; + + void on_episode_end(const TorchState &final_state) override; + + private: + std::shared_ptr rollout_buffer; + std::shared_ptr hidden_state; + + TorchState last_state; + TorchAction last_action; + torch::Tensor last_continuous_log_prob; + torch::Tensor last_discrete_log_prob; + torch::Tensor last_actor_hidden; + + // raised at construction and by on_episode_end, consumed by the next on_transition + bool next_episode_start = true; + }; + +}// namespace arenai::agent + +#endif//ARENAI_LIQUID_PPO_COLLECTOR_H diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_factory.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_factory.cpp new file mode 100644 index 00000000..62fc4312 --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_factory.cpp @@ -0,0 +1,48 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./liquid_ppo_factory.h" + +using namespace arenai; +using namespace arenai::agent; + +namespace arenai::agent { + + LiquidPpoTorchAgentFactory::LiquidPpoTorchAgentFactory( + const int vision_height, const int vision_width, const int nb_sensors, + const int nb_continuous_actions, const int nb_discrete_actions, const torch::Device device, + const LiquidPpoHyperParams ¶ms) + : config(cli_fields_to_map(liquid_ppo_cli_fields(), params)), + actor(std::make_shared( + vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions, + params.hidden_size_sensors, params.vision_channels, params.group_norm_nums, + params.neuron_number, params.unfolding_steps, params.delta_t, params.initial_sigma, + params.initial_fire_proba)), + hidden_state(std::make_shared(actor)), + rollout_buffer(std::make_shared()), + collector(std::make_shared(rollout_buffer, hidden_state)), + agent(std::make_shared(actor, hidden_state, device, collector)), + trainer(std::make_shared( + actor, rollout_buffer, vision_height, vision_width, nb_sensors, nb_continuous_actions, + nb_discrete_actions, params.actor_learning_rate, params.critic_learning_rate, + params.hidden_size_sensors, params.vision_channels, params.group_norm_nums, + params.neuron_number, params.unfolding_steps, params.delta_t, device, + params.metric_window_size, params.gamma, params.gae_lambda, params.clip_epsilon, + params.target_kl, params.grad_norm_max, params.continuous_target_entropy, + params.discrete_target_entropy_factor, params.epochs, params.rollout_size, + params.minibatch_size, params.chunk_size)) {} + + std::shared_ptr LiquidPpoTorchAgentFactory::get_agent() { return agent; } + + std::shared_ptr LiquidPpoTorchAgentFactory::get_collector() { + return collector; + } + + std::shared_ptr LiquidPpoTorchAgentFactory::get_trainer() { return trainer; } + + std::map LiquidPpoTorchAgentFactory::get_config() const { + return config; + } + +}// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_factory.h b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_factory.h new file mode 100644 index 00000000..03395fc2 --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_factory.h @@ -0,0 +1,45 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_LIQUID_PPO_FACTORY_H +#define ARENAI_LIQUID_PPO_FACTORY_H + +#include "../torch_factory.h" +#include "./liquid_hidden_state.h" +#include "./liquid_ppo_agent.h" +#include "./liquid_ppo_collector.h" +#include "./liquid_ppo_hyperparams.h" +#include "./liquid_ppo_rollout_buffer.h" +#include "./liquid_ppo_trainer.h" + +namespace arenai::agent { + + class LiquidPpoTorchAgentFactory final : public AbstractTorchAgentFactory { + public: + LiquidPpoTorchAgentFactory( + int vision_height, int vision_width, int nb_sensors, int nb_continuous_actions, + int nb_discrete_actions, torch::Device device, const LiquidPpoHyperParams ¶ms); + + std::shared_ptr get_agent() override; + std::shared_ptr get_collector() override; + std::shared_ptr get_trainer() override; + + std::map get_config() const override; + + private: + std::map config; + + // triad built once, sharing actor + hidden state + rollout_buffer + std::shared_ptr actor; + std::shared_ptr hidden_state; + + std::shared_ptr rollout_buffer; + std::shared_ptr collector; + std::shared_ptr agent; + std::shared_ptr trainer; + }; + +}// namespace arenai::agent + +#endif//ARENAI_LIQUID_PPO_FACTORY_H diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.cpp new file mode 100644 index 00000000..dfab2daf --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.cpp @@ -0,0 +1,39 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./liquid_ppo_hyperparams.h" + +namespace arenai::agent { + + std::vector> liquid_ppo_cli_fields() { + return { + {.name = "--actor_learning_rate", .member = &LiquidPpoHyperParams::actor_learning_rate}, + {.name = "--critic_learning_rate", + .member = &LiquidPpoHyperParams::critic_learning_rate}, + {.name = "--hidden_size_sensors", .member = &LiquidPpoHyperParams::hidden_size_sensors}, + {.name = "--vision_channels", .member = &LiquidPpoHyperParams::vision_channels}, + {.name = "--group_norm_nums", .member = &LiquidPpoHyperParams::group_norm_nums}, + {.name = "--neuron_number", .member = &LiquidPpoHyperParams::neuron_number}, + {.name = "--unfolding_steps", .member = &LiquidPpoHyperParams::unfolding_steps}, + {.name = "--delta_t", .member = &LiquidPpoHyperParams::delta_t}, + {.name = "--chunk_size", .member = &LiquidPpoHyperParams::chunk_size}, + {.name = "--initial_sigma", .member = &LiquidPpoHyperParams::initial_sigma}, + {.name = "--initial_fire_proba", .member = &LiquidPpoHyperParams::initial_fire_proba}, + {.name = "--metric_window_size", .member = &LiquidPpoHyperParams::metric_window_size}, + {.name = "--gamma", .member = &LiquidPpoHyperParams::gamma}, + {.name = "--gae_lambda", .member = &LiquidPpoHyperParams::gae_lambda}, + {.name = "--clip_epsilon", .member = &LiquidPpoHyperParams::clip_epsilon}, + {.name = "--target_kl", .member = &LiquidPpoHyperParams::target_kl}, + {.name = "--grad_norm_max", .member = &LiquidPpoHyperParams::grad_norm_max}, + {.name = "--continuous_target_entropy", + .member = &LiquidPpoHyperParams::continuous_target_entropy}, + {.name = "--discrete_target_entropy_factor", + .member = &LiquidPpoHyperParams::discrete_target_entropy_factor}, + {.name = "--epochs", .member = &LiquidPpoHyperParams::epochs}, + {.name = "--rollout_size", .member = &LiquidPpoHyperParams::rollout_size}, + {.name = "--minibatch_size", .member = &LiquidPpoHyperParams::minibatch_size}, + }; + } + +}// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h new file mode 100644 index 00000000..0f39c235 --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h @@ -0,0 +1,48 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_LIQUID_PPO_HYPERPARAMS_H +#define ARENAI_LIQUID_PPO_HYPERPARAMS_H + +#include +#include + +#include "../../utils/cli_fields.h" + +namespace arenai::agent { + + // Member initializers are the CLI defaults (single source of truth). + struct LiquidPpoHyperParams { + float actor_learning_rate = 1e-4f; + float critic_learning_rate = 3e-4f; + int hidden_size_sensors = 128; + std::vector> vision_channels = {{3, 8}, {8, 16}, {16, 24}, + {24, 32}, {32, 48}, {48, 64}}; + std::vector group_norm_nums = {1, 2, 3, 4, 6, 8}; + int neuron_number = 512; + int unfolding_steps = 6; + float delta_t = 1.f / 30.f; + // TBPTT window: length of the contiguous sequences the updates run on + int chunk_size = 30; + float initial_sigma = 0.5f; + float initial_fire_proba = 0.4f; + int metric_window_size = 256; + float gamma = 0.997f; + float gae_lambda = 0.99f; + float clip_epsilon = 0.2f; + float target_kl = 0.05f; + float grad_norm_max = 0.5f; + float continuous_target_entropy = 0.4f; + float discrete_target_entropy_factor = 0.2f; + int epochs = 2; + int rollout_size = 30 * 30; + // expressed in steps: a minibatch holds minibatch_size / chunk_size chunks + int minibatch_size = 1024; + }; + + std::vector> liquid_ppo_cli_fields(); + +}// namespace arenai::agent + +#endif//ARENAI_LIQUID_PPO_HYPERPARAMS_H diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.cpp new file mode 100644 index 00000000..376a0efc --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.cpp @@ -0,0 +1,108 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./liquid_ppo_rollout_buffer.h" + +using namespace arenai; +using namespace arenai::agent; + +namespace arenai::agent { + + namespace { + TorchState to_cpu(const TorchState &state) { + return { + .vision = state.vision.detach().cpu(), + .proprioception = state.proprioception.detach().cpu()}; + } + }// namespace + + void LiquidPpoRolloutBuffer::add(const LiquidPpoInputStep &step) { + const auto nb_tanks = step.state.vision.size(0); + + if (!already_terminated_.defined()) + already_terminated_ = torch::zeros( + {nb_tanks}, torch::TensorOptions().dtype(torch::kBool).device(torch::kCPU)); + + // tanks already terminated before this step have no valid transition to store + const auto valid = already_terminated_.logical_not(); + already_terminated_.logical_or_( + step.done.detach().cpu().to(torch::kBool).reshape({nb_tanks})); + + steps_.push_back( + {.step = + {.state = to_cpu(step.state), + .action = + {.continuous_action = step.action.continuous_action.detach().cpu(), + .discrete_action = step.action.discrete_action.detach().cpu()}, + .continuous_log_prob = step.continuous_log_prob.detach().cpu(), + .discrete_log_prob = step.discrete_log_prob.detach().cpu(), + .reward = step.reward.detach().cpu(), + .done = step.done.detach().cpu(), + .actor_hidden = step.actor_hidden.detach().cpu(), + .episode_start = step.episode_start}, + .valid = valid}); + + // the freshly added step is pending: its closing observation is not known yet + final_state_.reset(); + } + + void LiquidPpoRolloutBuffer::finish_episode(const TorchState &final_state) { + if (!steps_.empty()) final_state_ = to_cpu(final_state); + + if (already_terminated_.defined()) already_terminated_.fill_(false); + } + + size_t LiquidPpoRolloutBuffer::nb_complete_steps() const { + if (steps_.empty()) return 0; + return final_state_.has_value() ? steps_.size() : steps_.size() - 1; + } + + LiquidPpoRollout LiquidPpoRolloutBuffer::get_rollout() { + const auto nb_steps = nb_complete_steps(); + TORCH_CHECK(nb_steps > 0, "LiquidPpoRolloutBuffer: no complete step to train on"); + + // the observation closing the last consumed step: the episode's final state, + // or the pending step's own observation + const auto bootstrap_state = + final_state_.has_value() ? *final_state_ : steps_[nb_steps].step.state; + + const auto stack = [&](const auto &get_field) { + std::vector tensors; + tensors.reserve(nb_steps); + for (size_t i = 0; i < nb_steps; i++) tensors.push_back(get_field(steps_[i])); + return torch::stack(tensors, 0); + }; + + auto episode_starts = torch::zeros({static_cast(nb_steps)}, torch::kBool); + for (size_t i = 0; i < nb_steps; i++) + if (steps_[i].step.episode_start) episode_starts[static_cast(i)] = true; + + LiquidPpoRollout rollout{ + .states = + {.vision = stack([](const StoredStep &s) { return s.step.state.vision; }), + .proprioception = + stack([](const StoredStep &s) { return s.step.state.proprioception; })}, + .actions = + {.continuous_action = + stack([](const StoredStep &s) { return s.step.action.continuous_action; }), + .discrete_action = + stack([](const StoredStep &s) { return s.step.action.discrete_action; })}, + .continuous_log_probs = + stack([](const StoredStep &s) { return s.step.continuous_log_prob; }), + .discrete_log_probs = + stack([](const StoredStep &s) { return s.step.discrete_log_prob; }), + .rewards = stack([](const StoredStep &s) { return s.step.reward; }), + .dones = stack([](const StoredStep &s) { return s.step.done; }), + .bootstrap_state = bootstrap_state, + .valids = stack([](const StoredStep &s) { return s.valid; }).unsqueeze(-1), + .actor_hiddens = stack([](const StoredStep &s) { return s.step.actor_hidden; }), + .episode_starts = episode_starts}; + + steps_.erase(steps_.begin(), steps_.begin() + static_cast(nb_steps)); + final_state_.reset(); + + return rollout; + } + +}// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.h b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.h new file mode 100644 index 00000000..8d5117d6 --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.h @@ -0,0 +1,77 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_LIQUID_PPO_ROLLOUT_BUFFER_H +#define ARENAI_LIQUID_PPO_ROLLOUT_BUFFER_H + +#include +#include + +#include + +#include "../torch_types.h" + +namespace arenai::agent { + + struct LiquidPpoInputStep { + TorchState state; + TorchAction action; + torch::Tensor continuous_log_prob; + torch::Tensor discrete_log_prob; + torch::Tensor reward; + torch::Tensor done; + // [nb_tanks, neuron_number] actor liquid state opening the step + torch::Tensor actor_hidden; + // the step opens a fresh episode (the liquid states were re-drawn) + bool episode_start; + }; + + // On-policy rollout stacked on the time dimension: every tensor is [T, nb_tanks, ...] + struct LiquidPpoRollout { + TorchState states; + TorchAction actions; + torch::Tensor continuous_log_probs; + torch::Tensor discrete_log_probs; + torch::Tensor rewards; + torch::Tensor dones; + // [nb_tanks, ...] observation closing the last step, for the value bootstrap + TorchState bootstrap_state; + // [T, nb_tanks, 1] whether the (step, tank) pair is a live transition + torch::Tensor valids; + // [T, nb_tanks, neuron_number] actor liquid state opening each step + torch::Tensor actor_hiddens; + // [T] bool, the step opens a fresh episode + torch::Tensor episode_starts; + }; + + // Sequential on-policy buffer. A step is "complete" once the observation that + // follows it is known - the next add()'s state, or finish_episode()'s final state. + class LiquidPpoRolloutBuffer { + public: + void add(const LiquidPpoInputStep &step); + void finish_episode(const TorchState &final_state); + + size_t nb_complete_steps() const; + + // stacks and removes every complete step; the pending one (if any) stays + LiquidPpoRollout get_rollout(); + + private: + struct StoredStep { + LiquidPpoInputStep step; + torch::Tensor valid; + }; + + std::vector steps_; + + // observation closing the last stored step, set by finish_episode() + std::optional final_state_; + + // [nb_tanks] tanks already done in the current episode + torch::Tensor already_terminated_; + }; + +}// namespace arenai::agent + +#endif//ARENAI_LIQUID_PPO_ROLLOUT_BUFFER_H diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp new file mode 100644 index 00000000..b853462c --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp @@ -0,0 +1,430 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./liquid_ppo_trainer.h" + +#include +#include + +#include "../../distributions/multinomial.h" +#include "../../distributions/truncated_normal.h" +#include "../../metrics/mean_metric.h" +#include "../../networks/constants.h" +#include "../../networks_utils/print_module.h" +#include "../../networks_utils/torch_saver.h" + +using namespace arenai; +using namespace arenai::agent; + +namespace arenai::agent { + namespace { + // merges the [T, nb_tanks] leading dimensions into a single row dimension + torch::Tensor flatten_steps(const torch::Tensor &tensor) { + auto sizes = tensor.sizes().vec(); + sizes.erase(sizes.begin()); + sizes[0] = tensor.size(0) * tensor.size(1); + return tensor.reshape(sizes); + } + + constexpr float LOG_RATIO_MAX_ABS = 3.f; + + constexpr float KL_TRIM_FRACTION = 0.01f; + + constexpr float CONTINUOUS_ALPHA_K_P = 2e-1f; + constexpr float CONTINUOUS_ALPHA_K_I = 5e-3f; + constexpr float CONTINUOUS_ALPHA_K_D = 1.f; + + constexpr float DISCRETE_ALPHA_K_P = 2e-1f; + constexpr float DISCRETE_ALPHA_K_I = 1e-2f; + constexpr float DISCRETE_ALPHA_K_D = 1.f; + + constexpr float ALPHA_INITIAL = 1e-3f; + + }// namespace + + LiquidPpoTrainer::LiquidPpoTrainer( + const std::shared_ptr &actor, + const std::shared_ptr &rollout_buffer, const int vision_height, + const int vision_width, const int nb_sensors, const int nb_continuous_actions, + const int nb_discrete_action, const float actor_learning_rate, + const float critic_learning_rate, const int hidden_size_sensors, + const std::vector> &vision_channels, + const std::vector &group_norm_nums, const int neuron_number, const int unfolding_steps, + const float delta_t, const torch::Device device, const int metric_window_size, + const float gamma, const float gae_lambda, const float clip_epsilon, const float target_kl, + const float grad_norm_max, const float continuous_target_entropy, + const float discrete_target_entropy_factor, const int epochs, const int rollout_size, + const int minibatch_size, const int chunk_size) + : actor(actor), rollout_buffer(rollout_buffer), + continuous_alpha(std::make_unique( + CONTINUOUS_ALPHA_K_P, CONTINUOUS_ALPHA_K_I, CONTINUOUS_ALPHA_K_D, ALPHA_INITIAL, + nb_continuous_actions)), + discrete_alpha(std::make_unique( + DISCRETE_ALPHA_K_P, DISCRETE_ALPHA_K_I, DISCRETE_ALPHA_K_D, ALPHA_INITIAL, 1)), + continuous_target_entropy(continuous_target_entropy), + discrete_target_entropy( + discrete_target_entropy_factor * multinomial_maximum_entropy(nb_discrete_action)), + critic(std::make_shared( + vision_height, vision_width, nb_sensors, hidden_size_sensors, vision_channels, + group_norm_nums, neuron_number, unfolding_steps, delta_t)), + actor_optim( + std::make_unique(this->actor->parameters(), actor_learning_rate)), + critic_optim( + std::make_unique(critic->parameters(), critic_learning_rate)), + actor_mean_loss_metric(std::make_shared("π", metric_window_size)), + critic_mean_loss_metric(std::make_shared("v", metric_window_size)), + explained_variance_metric(std::make_shared("ev", metric_window_size)), + continuous_entropy_metric(std::make_shared("Hc", metric_window_size)), + discrete_entropy_metric(std::make_shared("Hd", metric_window_size)), + continuous_alpha_metric(std::make_shared("α_c", metric_window_size, 2, true)), + discrete_alpha_metric(std::make_shared("α_d", metric_window_size, 2, true)), + clip_fraction_metric(std::make_shared("clip", metric_window_size)), + kl_metric(std::make_shared("kl", metric_window_size, 2, true)), + skip_fraction_metric(std::make_shared("skip", metric_window_size)), + gamma(gamma), gae_lambda(gae_lambda), clip_epsilon(clip_epsilon), target_kl(target_kl), + grad_norm_max(grad_norm_max), epochs(epochs), rollout_size(rollout_size), + minibatch_size(minibatch_size), chunk_size(chunk_size) { + to(device); + + set_train(false); + } + + void LiquidPpoTrainer::step() { + if (rollout_buffer->nb_complete_steps() >= static_cast(rollout_size)) train(); + } + + void LiquidPpoTrainer::train() { + const auto device = actor->parameters().back().device(); + + const auto rollout = rollout_buffer->get_rollout(); + + set_train(false); + const auto [advantages, returns, critic_hiddens] = compute_gae(rollout, device); + + set_train(true); + + const auto nb_steps = rollout.rewards.size(0); + const auto nb_tanks = rollout.rewards.size(1); + + // contiguous chunk_size-long windows per tank; the (at most chunk_size - 1) + // trailing steps that cannot form a full chunk are dropped + const auto nb_chunks_per_tank = nb_steps / chunk_size; + if (nb_chunks_per_tank == 0) return; + const auto nb_kept_steps = nb_chunks_per_tank * chunk_size; + + // the rollout stays on CPU as flat [T * nb_tanks, ...] views; only the + // minibatches hit the device + const auto flat = [&](const torch::Tensor &tensor) { + return flatten_steps(tensor.slice(0, 0, nb_kept_steps)); + }; + + const auto flat_vision = flat(rollout.states.vision); + const auto flat_proprioception = flat(rollout.states.proprioception); + const auto flat_continuous_actions = flat(rollout.actions.continuous_action); + const auto flat_discrete_actions = flat(rollout.actions.discrete_action); + const auto flat_old_log_probs = + flat(rollout.continuous_log_probs) + flat(rollout.discrete_log_probs); + const auto flat_advantages = flat(advantages); + const auto flat_returns = flat(returns); + const auto flat_valids = flat(rollout.valids); + const auto flat_actor_hiddens = flat(rollout.actor_hiddens); + const auto flat_critic_hiddens = flat(critic_hiddens); + + // chunk unit u = chunk * nb_tanks + tank; the ones without any live + // transition bring nothing to train on + const auto chunk_valid_counts = rollout.valids.slice(0, 0, nb_kept_steps) + .reshape({nb_chunks_per_tank, chunk_size, nb_tanks}) + .sum(1) + .flatten(); + const auto valid_chunk_idx = torch::nonzero(chunk_valid_counts > 0).squeeze(-1); + const auto nb_valid_chunks = valid_chunk_idx.size(0); + if (nb_valid_chunks == 0) return; + + const auto chunks_per_minibatch = std::max(1, minibatch_size / chunk_size); + const auto step_offsets = torch::arange(chunk_size); + + for (int e = 0; e < epochs; e++) { + const auto perm = valid_chunk_idx.index_select(0, torch::randperm(nb_valid_chunks)); + + for (int64_t start = 0; start < nb_valid_chunks; start += chunks_per_minibatch) { + const auto idx = perm.slice( + 0, start, std::min(start + chunks_per_minibatch, nb_valid_chunks)); + + const auto tank = idx.remainder(nb_tanks); + const auto start_step = idx.div(nb_tanks, "floor") * chunk_size; + + // flat rows of the minibatch's chunks and of their first steps + const auto rows = ((start_step.unsqueeze(1) + step_offsets.unsqueeze(0)) * nb_tanks + + tank.unsqueeze(1)) + .flatten(); + const auto start_rows = start_step * nb_tanks + tank; + + // [nb_chunks, chunk_size, ...] + const auto select_chunks = [&](const torch::Tensor &flat_tensor) { + auto sizes = flat_tensor.sizes().vec(); + sizes[0] = idx.size(0); + sizes.insert(sizes.begin() + 1, chunk_size); + return flat_tensor.index_select(0, rows).reshape(sizes).to(device); + }; + // [nb_chunks, neuron_number] + const auto select_hiddens = [&](const torch::Tensor &flat_hiddens) { + return flat_hiddens.index_select(0, start_rows).to(device); + }; + + const auto mb_vision = select_chunks(flat_vision); + const auto mb_proprioception = select_chunks(flat_proprioception); + const auto mb_valids = select_chunks(flat_valids); + + train_actor( + mb_vision, mb_proprioception, select_hiddens(flat_actor_hiddens), + select_chunks(flat_continuous_actions), select_chunks(flat_discrete_actions), + select_chunks(flat_old_log_probs), select_chunks(flat_advantages), mb_valids); + + train_critic( + mb_vision, mb_proprioception, select_hiddens(flat_critic_hiddens), + select_chunks(flat_returns), mb_valids); + } + } + + set_train(false); + } + + bool LiquidPpoTrainer::train_actor( + const torch::Tensor &vision, const torch::Tensor &proprioception, + const torch::Tensor &hidden, const torch::Tensor &continuous_actions, + const torch::Tensor &discrete_actions, const torch::Tensor &old_log_probs, + const torch::Tensor &advantages, const torch::Tensor &valids) const { + const auto device = actor->parameters().back().device(); + + const auto out = actor->act_sequence(vision, proprioception, hidden); + + // recurrence done: fold time into rows and keep the live transitions only + const auto valid_idx = torch::nonzero(valids.flatten(0, 1).squeeze(-1)).squeeze(-1); + const auto rows = [&](const torch::Tensor &tensor) { + return tensor.flatten(0, 1).index_select(0, valid_idx); + }; + + const auto mu = rows(out.mu); + const auto sigma = rows(out.sigma); + const auto discrete_proba = rows(out.discrete); + + const auto curr_continuous_log_probs = + truncated_normal_log_pdf(rows(continuous_actions), mu, sigma).sum(-1, true); + + const auto clamped_proba = torch::clamp(discrete_proba, EPSILON, 1.0 - EPSILON); + const auto curr_discrete_log_probs = + torch::sum(rows(discrete_actions) * torch::log(clamped_proba), -1, true); + + const auto log_ratio = torch::clamp( + curr_continuous_log_probs + curr_discrete_log_probs - rows(old_log_probs), + -LOG_RATIO_MAX_ABS, LOG_RATIO_MAX_ABS); + + const auto ratio = torch::exp(log_ratio); + + const auto continuous_entropy = truncated_normal_entropy(mu, sigma); + const auto discrete_entropy = multinomial_entropy(discrete_proba); + + const auto kl_per_row = (ratio - 1.f - log_ratio).flatten(); + + const auto nb_kept = std::max( + 1, static_cast( + static_cast(kl_per_row.size(0)) * (1.f - KL_TRIM_FRACTION))); + + // per-row KL is non-negative: sorting ascending puts the outliers past nb_kept + const auto approx_kl = + std::get<0>(torch::sort(kl_per_row)).slice(0, 0, nb_kept).mean().item(); + + const bool kl_exceeded = target_kl > 0.f && approx_kl > 1.5f * target_kl; + + const auto entropy_bonus = + torch::sum(continuous_alpha->alpha().detach() * continuous_entropy, -1) + + discrete_alpha->alpha().squeeze(1).detach() * discrete_entropy; + + if (!kl_exceeded) { + const auto clipped_ratio = torch::clamp(ratio, 1.f - clip_epsilon, 1.f + clip_epsilon); + const auto valid_advantages = rows(advantages); + const auto surrogate = + torch::min(ratio * valid_advantages, clipped_ratio * valid_advantages); + + const auto actor_loss = -torch::mean(surrogate + entropy_bonus); + + actor_optim->zero_grad(); + actor_loss.backward(); + torch::nn::utils::clip_grad_norm_(actor->parameters(), grad_norm_max); + actor_optim->step(); + + // actor metrics + actor_mean_loss_metric->add(actor_loss.cpu().item()); + } + + // adjust alphas + continuous_alpha->update( + continuous_entropy, torch::tensor(continuous_target_entropy, device)); + discrete_alpha->update(discrete_entropy, torch::tensor(discrete_target_entropy, device)); + + // metrics + continuous_entropy_metric->add(continuous_entropy.mean().item()); + discrete_entropy_metric->add(discrete_entropy.mean().item()); + + continuous_alpha_metric->add(continuous_alpha->alpha().mean().item()); + discrete_alpha_metric->add(discrete_alpha->alpha().mean().item()); + + kl_metric->add(approx_kl); + + clip_fraction_metric->add( + ((ratio - 1.f).abs() > clip_epsilon).to(torch::kFloat).mean().item()); + + skip_fraction_metric->add(kl_exceeded ? 1.f : 0.f); + + return kl_exceeded; + } + + void LiquidPpoTrainer::train_critic( + const torch::Tensor &vision, const torch::Tensor &proprioception, + const torch::Tensor &hidden, const torch::Tensor &returns, + const torch::Tensor &valids) const { + const auto out = critic->value_sequence(vision, proprioception, hidden); + + // recurrence done: fold time into rows and keep the live transitions only + const auto valid_idx = torch::nonzero(valids.flatten(0, 1).squeeze(-1)).squeeze(-1); + const auto values = out.value.flatten(0, 1).index_select(0, valid_idx); + const auto valid_returns = returns.flatten(0, 1).index_select(0, valid_idx); + + const auto critic_loss = torch::mse_loss(values, valid_returns, at::Reduction::Mean); + + critic_optim->zero_grad(); + critic_loss.backward(); + torch::nn::utils::clip_grad_norm_(critic->parameters(), grad_norm_max); + critic_optim->step(); + + critic_mean_loss_metric->add(critic_loss.cpu().item()); + + const auto residual_var = (valid_returns - values.detach()).var(false); + const auto returns_var = valid_returns.var(false); + explained_variance_metric->add( + (1.f - residual_var / returns_var.clamp_min(EPSILON)).item()); + } + + LiquidGaeResult + LiquidPpoTrainer::compute_gae(const LiquidPpoRollout &rollout, const torch::Device device) { + torch::NoGradGuard no_grad; + + const auto nb_steps = rollout.rewards.size(0); + const auto nb_tanks = rollout.rewards.size(1); + + const auto episode_starts = rollout.episode_starts.accessor(); + + if (!critic_carry.defined() || critic_carry.size(0) != nb_tanks) + critic_carry = critic->initial_state(static_cast(nb_tanks)); + + // sequential critic pass: the values and the liquid state opening each step + std::vector value_list; + std::vector hidden_list; + value_list.reserve(nb_steps); + hidden_list.reserve(nb_steps); + + for (int64_t t = 0; t < nb_steps; t++) { + if (episode_starts[t]) critic_carry = critic->initial_state(static_cast(nb_tanks)); + + hidden_list.push_back(critic_carry.cpu()); + + const auto out = critic->value( + rollout.states.vision[t].to(device), rollout.states.proprioception[t].to(device), + critic_carry); + + value_list.push_back(out.value.cpu()); + critic_carry = out.next_x; + } + + const auto values = torch::stack(value_list, 0); + const auto critic_hiddens = torch::stack(hidden_list, 0); + + // the carry is NOT advanced past the bootstrap: its observation opens the + // next rollout's first step + const auto bootstrap_value = + critic + ->value( + rollout.bootstrap_state.vision.to(device), + rollout.bootstrap_state.proprioception.to(device), critic_carry) + .value.cpu() + .unsqueeze(0); + + // next values are the values shifted by one step, closed by the bootstrap state + const auto next_values = torch::cat({values.slice(0, 1), bootstrap_value}, 0); + + const auto rewards = rollout.rewards.to(torch::kFloat); + const auto dones = rollout.dones.to(torch::kFloat); + const auto valids = rollout.valids.to(torch::kFloat); + + const auto deltas = rewards + gamma * next_values * (1.f - dones) - values; + + auto advantages = torch::zeros_like(deltas); + auto gae = torch::zeros({nb_tanks, 1}, deltas.options()); + for (int64_t t = nb_steps - 1; t >= 0; t--) { + gae = deltas[t] + gamma * gae_lambda * (1.f - dones[t]) * gae; + advantages[t] = gae; + } + + const auto returns = advantages + values; + + const auto nb_valid = valids.sum().clamp_min(1.f); + const auto advantage_mean = torch::sum(advantages * valids) / nb_valid; + const auto advantage_std = + torch::sqrt(torch::sum(torch::square(advantages - advantage_mean) * valids) / nb_valid); + advantages = (advantages - advantage_mean) / (advantage_std + EPSILON); + + return {.advantages = advantages, .returns = returns, .critic_hiddens = critic_hiddens}; + } + + std::vector> LiquidPpoTrainer::get_metrics() { + return {actor_mean_loss_metric, critic_mean_loss_metric, explained_variance_metric, + continuous_entropy_metric, continuous_alpha_metric, discrete_entropy_metric, + discrete_alpha_metric, clip_fraction_metric, kl_metric, + skip_fraction_metric}; + } + + void LiquidPpoTrainer::save(const std::filesystem::path &output_folder) { + // Models + save_torch(output_folder, actor, "actor.pt"); + save_torch(output_folder, critic, "critic.pt"); + + // Optimizers + save_torch(output_folder, actor_optim, "actor_optim.pt"); + save_torch(output_folder, critic_optim, "critic_optim.pt"); + + // string repr + std::ostringstream actor_repr_oss; + dump_module_tree(actor, actor_repr_oss, 0, "actor"); + std::ofstream actor_repr_file(output_folder / "actor_repr.txt"); + actor_repr_file << actor_repr_oss.str(); + actor_repr_file.close(); + + std::ostringstream critic_repr_oss; + dump_module_tree(critic, critic_repr_oss, 0, "critic"); + std::ofstream critic_repr_file(output_folder / "critic_repr.txt"); + critic_repr_file << critic_repr_oss.str(); + critic_repr_file.close(); + } + + void LiquidPpoTrainer::set_train(const bool train) const { + actor->train(train); + critic->train(train); + + continuous_alpha->train(train); + discrete_alpha->train(train); + } + + void LiquidPpoTrainer::to(const torch::Device device) const { + actor->to(device); + critic->to(device); + + continuous_alpha->to(device); + discrete_alpha->to(device); + } + + int LiquidPpoTrainer::count_parameters() { + return count_parameters_impl(actor->parameters()) + + count_parameters_impl(critic->parameters()); + } +}// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.h b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.h new file mode 100644 index 00000000..0c64fb1e --- /dev/null +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.h @@ -0,0 +1,121 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_LIQUID_PPO_TRAINER_H +#define ARENAI_LIQUID_PPO_TRAINER_H + +#include "../../networks/entropy.h" +#include "../../networks/recurrent/liquid_actor.h" +#include "../../networks/recurrent/liquid_critic.h" +#include "../trainer.h" +#include "./liquid_ppo_rollout_buffer.h" + +namespace arenai::agent { + + struct LiquidGaeResult { + torch::Tensor advantages; + torch::Tensor returns; + // [T, nb_tanks, neuron_number] critic liquid state opening each step + torch::Tensor critic_hiddens; + }; + + // Recurrent PPO: the updates run on contiguous [chunk_size]-long sequences + // (truncated BPTT), each initialized with the liquid state recorded when the + // chunk's first step was collected. + class LiquidPpoTrainer final : public AbstractTrainer { + public: + LiquidPpoTrainer( + const std::shared_ptr &actor, + const std::shared_ptr &rollout_buffer, int vision_height, + int vision_width, int nb_sensors, int nb_continuous_actions, int nb_discrete_action, + float actor_learning_rate, float critic_learning_rate, int hidden_size_sensors, + const std::vector> &vision_channels, + const std::vector &group_norm_nums, int neuron_number, int unfolding_steps, + float delta_t, torch::Device device, int metric_window_size, float gamma, + float gae_lambda, float clip_epsilon, float target_kl, float grad_norm_max, + float continuous_target_entropy, float discrete_target_entropy_factor, int epochs, + int rollout_size, int minibatch_size, int chunk_size); + + void step() override; + + std::vector> get_metrics() override; + + void save(const std::filesystem::path &output_folder) override; + + int count_parameters() override; + + private: + std::shared_ptr actor; + std::shared_ptr rollout_buffer; + + std::unique_ptr continuous_alpha; + std::unique_ptr discrete_alpha; + + float continuous_target_entropy; + float discrete_target_entropy; + + std::shared_ptr critic; + + std::unique_ptr actor_optim; + std::unique_ptr critic_optim; + + std::shared_ptr actor_mean_loss_metric; + std::shared_ptr critic_mean_loss_metric; + + // share of the return variance the critic explains, per critic minibatch + std::shared_ptr explained_variance_metric; + + // both regulated by their constant entropy bonus + std::shared_ptr continuous_entropy_metric; + std::shared_ptr discrete_entropy_metric; + + std::shared_ptr continuous_alpha_metric; + std::shared_ptr discrete_alpha_metric; + + // both recorded on every attempted minibatch, skipped ones included + std::shared_ptr clip_fraction_metric; + std::shared_ptr kl_metric; + + // fraction of minibatches the KL threshold skipped + std::shared_ptr skip_fraction_metric; + + float gamma; + float gae_lambda; + float clip_epsilon; + float target_kl; + + float grad_norm_max; + + int epochs; + int rollout_size; + int minibatch_size; + int chunk_size; + + // critic liquid state opening the next rollout's first step, carried + // across train() calls; re-drawn at every episode start + torch::Tensor critic_carry; + + void train(); + + bool train_actor( + const torch::Tensor &vision, const torch::Tensor &proprioception, + const torch::Tensor &hidden, const torch::Tensor &continuous_actions, + const torch::Tensor &discrete_actions, const torch::Tensor &old_log_probs, + const torch::Tensor &advantages, const torch::Tensor &valids) const; + + void train_critic( + const torch::Tensor &vision, const torch::Tensor &proprioception, + const torch::Tensor &hidden, const torch::Tensor &returns, + const torch::Tensor &valids) const; + + // sequential critic pass over the rollout, advancing critic_carry + LiquidGaeResult compute_gae(const LiquidPpoRollout &rollout, torch::Device device); + + void set_train(bool train) const; + void to(torch::Device device) const; + }; + +}// namespace arenai::agent + +#endif//ARENAI_LIQUID_PPO_TRAINER_H diff --git a/arenai_agent/src/networks/recurrent/actor.h b/arenai_agent/src/networks/recurrent/actor.h deleted file mode 100644 index 03f65b27..00000000 --- a/arenai_agent/src/networks/recurrent/actor.h +++ /dev/null @@ -1,8 +0,0 @@ -// -// Created by samuel on 06/09/2026. -// - -#ifndef ARENAI_ACTOR_H -#define ARENAI_ACTOR_H - -#endif//ARENAI_ACTOR_H diff --git a/arenai_agent/src/networks/recurrent/critic.h b/arenai_agent/src/networks/recurrent/critic.h deleted file mode 100644 index 6877171d..00000000 --- a/arenai_agent/src/networks/recurrent/critic.h +++ /dev/null @@ -1,8 +0,0 @@ -// -// Created by samuel on 06/09/2026. -// - -#ifndef ARENAI_CRITIC_H -#define ARENAI_CRITIC_H - -#endif//ARENAI_CRITIC_H diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.cpp b/arenai_agent/src/networks/recurrent/liquid_actor.cpp new file mode 100644 index 00000000..f2e612bc --- /dev/null +++ b/arenai_agent/src/networks/recurrent/liquid_actor.cpp @@ -0,0 +1,110 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./liquid_actor.h" + +#include "../../networks_utils/init.h" +#include "../constants.h" +#include "../misc.h" + +using namespace arenai; +using namespace arenai::agent; + +namespace arenai::agent { + + LiquidActor::LiquidActor( + const int &vision_height, const int &vision_width, const int &nb_sensors, + const int &nb_continuous_actions, const int &nb_discrete_actions, + const int &hidden_size_sensors, const std::vector> &vision_channels, + const std::vector &group_norm_nums, const int &neuron_number, + const int &unfolding_steps, const float &delta_t, const float &initial_sigma, + const float &initial_fire_proba) + : vision_encoder(register_module( + "vision_encoder", std::make_shared( + vision_height, vision_width, vision_channels, group_norm_nums))), + sensors_encoder(register_module( + "sensors_encoder", + torch::nn::Sequential( + torch::nn::Linear( + torch::nn::LinearOptions(nb_sensors, hidden_size_sensors).bias(false)), + torch::nn::LayerNorm(torch::nn::LayerNormOptions({hidden_size_sensors})), + torch::nn::SiLU()))), + liquid(register_module( + "liquid", std::make_shared( + neuron_number, hidden_size_sensors + vision_encoder->get_output_size(), + neuron_number, unfolding_steps, + [](const torch::Tensor &t) { return torch::tanh(t); }, delta_t))), + mu(register_module( + "mu", + torch::nn::Sequential( + torch::nn::Linear(neuron_number, nb_continuous_actions), torch::nn::Tanh()))), + sigma(register_module( + "sigma", torch::nn::Sequential( + torch::nn::Linear(neuron_number, nb_continuous_actions), + std::make_shared(SIGMA_MIN, SIGMA_MAX)))), + discrete(register_module( + "discrete", + torch::nn::Sequential( + torch::nn::Linear(neuron_number, nb_discrete_actions), torch::nn::Softmax(-1)))) { + + vision_encoder->apply(init_hidden_weights); + sensors_encoder->apply(init_hidden_weights); + + mu->apply(init_mu_output_weights); + sigma->apply([initial_sigma](Module &m) { init_sigma_output_weights(m, initial_sigma); }); + + discrete->apply([initial_fire_proba](Module &m) { + init_discrete_output_weights(m, initial_fire_proba); + }); + } + + torch::Tensor LiquidActor::encode(const torch::Tensor &vision, const torch::Tensor &sensors) { + return torch::cat({vision_encoder->forward(vision), sensors_encoder->forward(sensors)}, 1); + } + + LiquidActorOutput LiquidActor::act( + const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t) { + const auto [output, next_x] = liquid->forward_step(x_t, encode(vision, sensors)); + return { + .mu = mu->forward(output), + .sigma = sigma->forward(output), + .discrete = discrete->forward(output), + .next_x = next_x}; + } + + LiquidActorOutput LiquidActor::act_sequence( + const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t) { + const auto batch_size = vision.size(0); + const auto nb_steps = vision.size(1); + + // the encoders are step-wise: fold time into the row dimension + const auto encoded = + encode(vision.flatten(0, 1), sensors.flatten(0, 1)).reshape({batch_size, nb_steps, -1}); + + auto x = x_t; + + std::vector outputs; + outputs.reserve(nb_steps); + + for (long t = 0; t < nb_steps; t++) { + auto [output, next_x] = liquid->forward_step( + x, encoded.index({at::indexing::Slice(), t, at::indexing::Slice()})); + + x = next_x; + outputs.push_back(output); + } + + const auto output = torch::stack(outputs, 1); + return { + .mu = mu->forward(output), + .sigma = sigma->forward(output), + .discrete = discrete->forward(output), + .next_x = x}; + } + + torch::Tensor LiquidActor::initial_state(const int batch_size) { + return liquid->get_first_x(batch_size); + } + +}// namespace arenai::agent diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.h b/arenai_agent/src/networks/recurrent/liquid_actor.h new file mode 100644 index 00000000..b2cd5376 --- /dev/null +++ b/arenai_agent/src/networks/recurrent/liquid_actor.h @@ -0,0 +1,65 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_LIQUID_ACTOR_H +#define ARENAI_LIQUID_ACTOR_H + +#include + +#include + +#include "../vision.h" +#include "./liquid_recurrent.h" + +namespace arenai::agent { + + struct LiquidActorOutput { + torch::Tensor mu; + torch::Tensor sigma; + torch::Tensor discrete; + // liquid state after the last processed step + torch::Tensor next_x; + }; + + // CNN on the frames, MLP on the proprioception, both concatenated into the + // recurrent input of each step, then run through the liquid network + class LiquidActor final : public torch::nn::Module { + public: + explicit LiquidActor( + const int &vision_height, const int &vision_width, const int &nb_sensors, + const int &nb_continuous_actions, const int &nb_discrete_actions, + const int &hidden_size_sensors, + const std::vector> &vision_channels, + const std::vector &group_norm_nums, const int &neuron_number, + const int &unfolding_steps, const float &delta_t, const float &initial_sigma, + const float &initial_fire_proba); + + // one env step: vision [B, C, H, W], sensors [B, S], x_t [B, neuron_number] + LiquidActorOutput + act(const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t); + + // BPTT chunk: vision [B, T, C, H, W], sensors [B, T, S], x_t [B, neuron_number]; + // outputs are [B, T, ...] + LiquidActorOutput act_sequence( + const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t); + + torch::Tensor initial_state(int batch_size); + + private: + std::shared_ptr vision_encoder; + torch::nn::Sequential sensors_encoder; + + std::shared_ptr liquid; + + torch::nn::Sequential mu; + torch::nn::Sequential sigma; + torch::nn::Sequential discrete; + + // [rows, features] recurrent input: encoded vision and sensors, concatenated + torch::Tensor encode(const torch::Tensor &vision, const torch::Tensor &sensors); + }; + +}// namespace arenai::agent + +#endif//ARENAI_LIQUID_ACTOR_H diff --git a/arenai_agent/src/networks/recurrent/liquid_critic.cpp b/arenai_agent/src/networks/recurrent/liquid_critic.cpp new file mode 100644 index 00000000..41d1168c --- /dev/null +++ b/arenai_agent/src/networks/recurrent/liquid_critic.cpp @@ -0,0 +1,81 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./liquid_critic.h" + +#include "../../networks_utils/init.h" + +using namespace arenai; +using namespace arenai::agent; + +namespace arenai::agent { + + LiquidCritic::LiquidCritic( + const int &vision_height, const int &vision_width, const int &nb_sensors, + const int &hidden_size_sensors, const std::vector> &vision_channels, + const std::vector &group_norm_nums, const int &neuron_number, + const int &unfolding_steps, const float &delta_t) + : vision_encoder(register_module( + "vision_encoder", std::make_shared( + vision_height, vision_width, vision_channels, group_norm_nums))), + sensors_encoder(register_module( + "sensors_encoder", + torch::nn::Sequential( + torch::nn::Linear( + torch::nn::LinearOptions(nb_sensors, hidden_size_sensors).bias(false)), + torch::nn::LayerNorm(torch::nn::LayerNormOptions({hidden_size_sensors})), + torch::nn::SiLU()))), + liquid(register_module( + "liquid", std::make_shared( + neuron_number, hidden_size_sensors + vision_encoder->get_output_size(), + neuron_number, unfolding_steps, + [](const torch::Tensor &t) { return torch::tanh(t); }, delta_t))), + to_value(register_module("to_value", torch::nn::Linear(neuron_number, 1))) { + + vision_encoder->apply(init_hidden_weights); + sensors_encoder->apply(init_hidden_weights); + + to_value->apply(init_value_output_weights); + } + + torch::Tensor LiquidCritic::encode(const torch::Tensor &vision, const torch::Tensor &sensors) { + return torch::cat({vision_encoder->forward(vision), sensors_encoder->forward(sensors)}, 1); + } + + LiquidCriticOutput LiquidCritic::value( + const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t) { + const auto [output, next_x] = liquid->forward_step(x_t, encode(vision, sensors)); + return {.value = to_value->forward(output), .next_x = next_x}; + } + + LiquidCriticOutput LiquidCritic::value_sequence( + const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t) { + const auto batch_size = vision.size(0); + const auto nb_steps = vision.size(1); + + // the encoders are step-wise: fold time into the row dimension + const auto encoded = + encode(vision.flatten(0, 1), sensors.flatten(0, 1)).reshape({batch_size, nb_steps, -1}); + + auto x = x_t; + + std::vector outputs; + outputs.reserve(nb_steps); + + for (long t = 0; t < nb_steps; t++) { + auto [output, next_x] = liquid->forward_step( + x, encoded.index({at::indexing::Slice(), t, at::indexing::Slice()})); + + x = next_x; + outputs.push_back(output); + } + + return {.value = to_value->forward(torch::stack(outputs, 1)), .next_x = x}; + } + + torch::Tensor LiquidCritic::initial_state(const int batch_size) { + return liquid->get_first_x(batch_size); + } + +}// namespace arenai::agent diff --git a/arenai_agent/src/networks/recurrent/liquid_critic.h b/arenai_agent/src/networks/recurrent/liquid_critic.h new file mode 100644 index 00000000..c61337d8 --- /dev/null +++ b/arenai_agent/src/networks/recurrent/liquid_critic.h @@ -0,0 +1,58 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_LIQUID_CRITIC_H +#define ARENAI_LIQUID_CRITIC_H + +#include + +#include + +#include "../vision.h" +#include "./liquid_recurrent.h" + +namespace arenai::agent { + + struct LiquidCriticOutput { + torch::Tensor value; + // liquid state after the last processed step + torch::Tensor next_x; + }; + + // same encoders as the liquid actor, ending on a state-value head + class LiquidCritic final : public torch::nn::Module { + public: + explicit LiquidCritic( + const int &vision_height, const int &vision_width, const int &nb_sensors, + const int &hidden_size_sensors, + const std::vector> &vision_channels, + const std::vector &group_norm_nums, const int &neuron_number, + const int &unfolding_steps, const float &delta_t); + + // one env step: vision [B, C, H, W], sensors [B, S], x_t [B, neuron_number] + LiquidCriticOutput + value(const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t); + + // BPTT chunk: vision [B, T, C, H, W], sensors [B, T, S], x_t [B, neuron_number]; + // value is [B, T, 1] + LiquidCriticOutput value_sequence( + const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t); + + torch::Tensor initial_state(int batch_size); + + private: + std::shared_ptr vision_encoder; + torch::nn::Sequential sensors_encoder; + + std::shared_ptr liquid; + + torch::nn::Linear to_value; + + // [rows, features] recurrent input: encoded vision and sensors, concatenated + torch::Tensor encode(const torch::Tensor &vision, const torch::Tensor &sensors); + }; + +}// namespace arenai::agent + +#endif//ARENAI_LIQUID_CRITIC_H diff --git a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp b/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp index c44a5e8a..f5a705c0 100644 --- a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp @@ -87,9 +87,10 @@ torch::Tensor LiquidRecurrent::forward(const torch::Tensor &inputs) { results.reserve(nb_steps); for (long t = 0; t < nb_steps; t++) { - x_t = cell->forward(x_t, inputs.index({at::indexing::Slice(), t, at::indexing::Slice()})); + auto [output, x_t_next] = + forward_step(x_t, inputs.index({at::indexing::Slice(), t, at::indexing::Slice()})); - const auto output = to_output->forward(x_t); + x_t = x_t_next; results.push_back(output); } @@ -97,6 +98,12 @@ torch::Tensor LiquidRecurrent::forward(const torch::Tensor &inputs) { return torch::stack(results, 1); } +std::tuple +LiquidRecurrent::forward_step(const torch::Tensor &x_t, const torch::Tensor &input_t) { + const auto x_t_next = cell->forward(x_t, input_t); + return {to_output->forward(x_t_next), x_t_next}; +} + torch::Tensor LiquidRecurrent::get_first_x(int batch_size) { return 1e-1 * torch::randn({batch_size, neuron_number}, parameters().back().device()); } diff --git a/arenai_agent/src/networks/recurrent/liquid_recurrent.h b/arenai_agent/src/networks/recurrent/liquid_recurrent.h index 67489243..7501cdba 100644 --- a/arenai_agent/src/networks/recurrent/liquid_recurrent.h +++ b/arenai_agent/src/networks/recurrent/liquid_recurrent.h @@ -53,13 +53,17 @@ class LiquidRecurrent : public torch::nn::Module { torch::Tensor forward(const torch::Tensor &inputs); + // one recurrent step, the state is handled by the caller: (output, x_t_next) + std::tuple + forward_step(const torch::Tensor &x_t, const torch::Tensor &input_t); + + torch::Tensor get_first_x(int batch_size); + private: std::shared_ptr cell; int neuron_number; torch::nn::Sequential to_output; - - torch::Tensor get_first_x(int batch_size); }; #endif//ARENAI_LIQUID_RECURRENT_H diff --git a/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo.h b/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo.h new file mode 100644 index 00000000..43331bad --- /dev/null +++ b/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo.h @@ -0,0 +1,44 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_TESTS_LIQUID_PPO_H +#define ARENAI_TESTS_LIQUID_PPO_H + +#include +#include + +#include +#include + +struct LiquidPpoTestConfig { + int vision_height; + int vision_width; + int nb_sensors; + int nb_continuous_actions; + int nb_discrete_actions; +}; + +class LiquidPpoAgentTest : public testing::Test { +protected: + void SetUp() override; + void TearDown() override; + + std::unique_ptr + make_factory(const LiquidPpoTestConfig &cfg) const; + + static arenai::agent::TorchState make_state(const LiquidPpoTestConfig &cfg, int batch); + + std::filesystem::path tmp_dir; + torch::Device device{torch::kCPU}; +}; + +typedef LiquidPpoTestConfig LiquidPpoActShapeParam; + +class LiquidPpoActShapeParamTest : public LiquidPpoAgentTest, + public testing::WithParamInterface {}; + +class LiquidPpoSaveLoadParamTest : public LiquidPpoAgentTest, + public testing::WithParamInterface {}; + +#endif//ARENAI_TESTS_LIQUID_PPO_H diff --git a/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo_rollout_buffer.h b/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo_rollout_buffer.h new file mode 100644 index 00000000..634d08ce --- /dev/null +++ b/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo_rollout_buffer.h @@ -0,0 +1,28 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_TESTS_LIQUID_PPO_ROLLOUT_BUFFER_H +#define ARENAI_TESTS_LIQUID_PPO_ROLLOUT_BUFFER_H + +#include +#include + +class LiquidPpoRolloutBufferTest : public testing::Test { +protected: + static constexpr int NB_TANKS = 2; + static constexpr int VISION_SIZE = 4; + static constexpr int NB_SENSORS = 3; + static constexpr int NB_CONTINUOUS_ACTIONS = 2; + static constexpr int NB_DISCRETE_ACTIONS = 2; + static constexpr int NEURON_NUMBER = 4; + + static arenai::agent::TorchState make_state(); + + static arenai::agent::LiquidPpoInputStep make_step( + const arenai::agent::TorchState &state, const torch::Tensor &done, bool episode_start); + + static arenai::agent::LiquidPpoInputStep make_step(const arenai::agent::TorchState &state); +}; + +#endif//ARENAI_TESTS_LIQUID_PPO_ROLLOUT_BUFFER_H diff --git a/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo_training.h b/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo_training.h new file mode 100644 index 00000000..6b58a0f5 --- /dev/null +++ b/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_liquid_ppo_training.h @@ -0,0 +1,41 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_TESTS_LIQUID_PPO_TRAINING_H +#define ARENAI_TESTS_LIQUID_PPO_TRAINING_H + +#include + +#include +#include + +struct LiquidPpoTrainingTestConfig { + int vision_height; + int vision_width; + int nb_sensors; + int nb_continuous_actions; + int nb_discrete_actions; +}; + +class LiquidPpoTrainingTest : public testing::Test { +protected: + // small enough for the trainer to trigger during the test loop + static constexpr int ROLLOUT_SIZE = 4; + // smaller than the number of valid rows so the loop exercises several minibatches + static constexpr int MINIBATCH_SIZE = 4; + static constexpr int CHUNK_SIZE = 2; + static constexpr int NEURON_NUMBER = 16; + static constexpr int UNFOLDING_STEPS = 2; + static constexpr float DELTA_T = 1.f / 30.f; + + torch::Device device{torch::kCPU}; + + std::unique_ptr + make_factory(const LiquidPpoTrainingTestConfig &cfg) const; + + static arenai::agent::TorchState + make_state(const LiquidPpoTrainingTestConfig &cfg, int nb_tanks); +}; + +#endif//ARENAI_TESTS_LIQUID_PPO_TRAINING_H diff --git a/arenai_agent/tests/src/tests_agents/tests_liquid_ppo.cpp b/arenai_agent/tests/src/tests_agents/tests_liquid_ppo.cpp new file mode 100644 index 00000000..8552e846 --- /dev/null +++ b/arenai_agent/tests/src/tests_agents/tests_liquid_ppo.cpp @@ -0,0 +1,195 @@ +// +// Created by samuel on 06/09/2026. +// + +#include + +using namespace arenai; +using namespace arenai::agent; + +// ======================================================================== +// Fixture helpers +// ======================================================================== + +void LiquidPpoAgentTest::SetUp() { + tmp_dir = std::filesystem::temp_directory_path() / "arenai_test_liquid_ppo"; + std::filesystem::create_directories(tmp_dir); +} + +void LiquidPpoAgentTest::TearDown() { std::filesystem::remove_all(tmp_dir); } + +std::unique_ptr +LiquidPpoAgentTest::make_factory(const LiquidPpoTestConfig &cfg) const { + const LiquidPpoHyperParams params{ + .actor_learning_rate = 1e-3f, + .critic_learning_rate = 1e-3f, + .hidden_size_sensors = 16, + .vision_channels = {{3, 8}}, + .group_norm_nums = {4}, + .neuron_number = 16, + .unfolding_steps = 2, + .chunk_size = 2, + .metric_window_size = 10, + .gamma = 0.99f, + .gae_lambda = 0.95f, + .clip_epsilon = 0.2f, + .grad_norm_max = 1.f, + .epochs = 1, + .rollout_size = 8, + .minibatch_size = 10}; + + return std::make_unique( + cfg.vision_height, cfg.vision_width, cfg.nb_sensors, cfg.nb_continuous_actions, + cfg.nb_discrete_actions, device, params); +} + +TorchState LiquidPpoAgentTest::make_state(const LiquidPpoTestConfig &cfg, const int batch) { + return { + .vision = + torch::randint(0, 255, {batch, 3, cfg.vision_height, cfg.vision_width}, torch::kUInt8), + .proprioception = torch::randn({batch, cfg.nb_sensors})}; +} + +// ======================================================================== +// Fixed tests +// ======================================================================== + +TEST_F(LiquidPpoAgentTest, ParameterCountPositive) { + constexpr LiquidPpoTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 10, + .nb_continuous_actions = 4, + .nb_discrete_actions = 2}; + const auto factory = make_factory(cfg); + + ASSERT_GT(factory->get_trainer()->count_parameters(), 0); +} + +TEST_F(LiquidPpoAgentTest, MetricsNotEmpty) { + constexpr LiquidPpoTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 10, + .nb_continuous_actions = 4, + .nb_discrete_actions = 2}; + const auto factory = make_factory(cfg); + + const auto metrics = factory->get_trainer()->get_metrics(); + + ASSERT_EQ(metrics.size(), 10); +} + +// ======================================================================== +// Parameterized: act shape tests +// ======================================================================== + +TEST_P(LiquidPpoActShapeParamTest, ActOutputShapes) { + const auto cfg = GetParam(); + const auto factory = make_factory(cfg); + + constexpr int batch = 4; + const auto [continuous_action, discrete_action] = + factory->get_agent()->act(make_state(cfg, batch), true); + + ASSERT_EQ(continuous_action.size(0), batch); + ASSERT_EQ(continuous_action.size(1), cfg.nb_continuous_actions); + + ASSERT_EQ(discrete_action.size(0), batch); + ASSERT_EQ(discrete_action.size(1), cfg.nb_discrete_actions); +} + +TEST_P(LiquidPpoActShapeParamTest, ActContinuousFinite) { + const auto cfg = GetParam(); + const auto factory = make_factory(cfg); + + constexpr int batch = 4; + const auto [continuous_action, discrete_action] = + factory->get_agent()->act(make_state(cfg, batch), true); + + ASSERT_TRUE(torch::all(torch::isfinite(continuous_action)).item()); +} + +TEST_P(LiquidPpoActShapeParamTest, ActDiscreteIsOneHot) { + const auto cfg = GetParam(); + const auto factory = make_factory(cfg); + + constexpr int batch = 4; + const auto [continuous_action, discrete_action] = + factory->get_agent()->act(make_state(cfg, batch), true); + + const auto row_sums = torch::sum(discrete_action, -1); + ASSERT_TRUE(torch::allclose(row_sums, torch::ones({batch}))); + + const auto is_binary = + torch::logical_or(torch::eq(discrete_action, 0.0f), torch::eq(discrete_action, 1.0f)); + ASSERT_TRUE(torch::all(is_binary).item()); +} + +TEST_P(LiquidPpoActShapeParamTest, ActKeepsShapesOverConsecutiveSteps) { + const auto cfg = GetParam(); + const auto factory = make_factory(cfg); + + // the liquid state is carried across the calls: every step must stay consistent + constexpr int batch = 4; + for (int t = 0; t < 3; t++) { + const auto [continuous_action, discrete_action] = + factory->get_agent()->act(make_state(cfg, batch), true); + + ASSERT_EQ(continuous_action.size(0), batch); + ASSERT_EQ(continuous_action.size(1), cfg.nb_continuous_actions); + ASSERT_TRUE(torch::all(torch::isfinite(continuous_action)).item()); + + ASSERT_EQ(discrete_action.size(0), batch); + ASSERT_EQ(discrete_action.size(1), cfg.nb_discrete_actions); + } +} + +INSTANTIATE_TEST_SUITE_P( + LiquidPpoAgent, LiquidPpoActShapeParamTest, + testing::Values( + LiquidPpoTestConfig{8, 8, 10, 4, 2}, LiquidPpoTestConfig{8, 8, 5, 2, 3}, + LiquidPpoTestConfig{16, 16, 20, 6, 4}, LiquidPpoTestConfig{8, 12, 10, 4, 2})); + +// ======================================================================== +// Parameterized: save / load tests +// ======================================================================== + +TEST_P(LiquidPpoSaveLoadParamTest, SaveCreatesExpectedFiles) { + const auto cfg = GetParam(); + const auto factory = make_factory(cfg); + + const auto save_dir = tmp_dir / "save_test"; + std::filesystem::create_directories(save_dir); + + factory->get_trainer()->save(save_dir); + + const std::vector expected_files = { + "actor.pt", "critic.pt", "actor_optim.pt", + "critic_optim.pt", "actor_repr.txt", "critic_repr.txt", + }; + + for (const auto &f: expected_files) + ASSERT_TRUE(std::filesystem::exists(save_dir / f)) << "Missing file: " << f; +} + +TEST_P(LiquidPpoSaveLoadParamTest, SavedFilesNonEmpty) { + const auto cfg = GetParam(); + const auto factory = make_factory(cfg); + + const auto save_dir = tmp_dir / "save_nonempty"; + std::filesystem::create_directories(save_dir); + + factory->get_trainer()->save(save_dir); + + for (const auto &entry: std::filesystem::directory_iterator(save_dir)) { + if (entry.is_regular_file()) + ASSERT_GT(entry.file_size(), 0u) << "Empty file: " << entry.path().filename(); + } +} + +INSTANTIATE_TEST_SUITE_P( + LiquidPpoAgent, LiquidPpoSaveLoadParamTest, + testing::Values( + LiquidPpoTestConfig{8, 8, 10, 4, 2}, LiquidPpoTestConfig{8, 8, 5, 2, 3}, + LiquidPpoTestConfig{16, 16, 20, 6, 4})); diff --git a/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_rollout_buffer.cpp b/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_rollout_buffer.cpp new file mode 100644 index 00000000..2cebe7e5 --- /dev/null +++ b/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_rollout_buffer.cpp @@ -0,0 +1,237 @@ +// +// Created by samuel on 06/09/2026. +// + +#include + +using namespace arenai; +using namespace arenai::agent; + +// ======================================================================== +// Fixture helpers +// ======================================================================== + +TorchState LiquidPpoRolloutBufferTest::make_state() { + return { + .vision = torch::randn({NB_TANKS, 3, VISION_SIZE, VISION_SIZE}), + .proprioception = torch::randn({NB_TANKS, NB_SENSORS})}; +} + +LiquidPpoInputStep LiquidPpoRolloutBufferTest::make_step( + const TorchState &state, const torch::Tensor &done, const bool episode_start) { + return { + .state = state, + .action = + {.continuous_action = torch::randn({NB_TANKS, NB_CONTINUOUS_ACTIONS}), + .discrete_action = torch::eye(NB_DISCRETE_ACTIONS) + .index_select( + 0, torch::randint( + NB_DISCRETE_ACTIONS, {NB_TANKS}, + torch::TensorOptions().dtype(torch::kInt64)))}, + .continuous_log_prob = torch::randn({NB_TANKS, 1}), + .discrete_log_prob = torch::randn({NB_TANKS, 1}), + .reward = torch::randn({NB_TANKS, 1}), + .done = done, + .actor_hidden = torch::randn({NB_TANKS, NEURON_NUMBER}), + .episode_start = episode_start}; +} + +LiquidPpoInputStep LiquidPpoRolloutBufferTest::make_step(const TorchState &state) { + return make_step(state, torch::zeros({NB_TANKS, 1}), false); +} + +// ======================================================================== +// Completion counting +// ======================================================================== + +TEST_F(LiquidPpoRolloutBufferTest, EmptyBufferHasNoCompleteStep) { + LiquidPpoRolloutBuffer buffer; + + ASSERT_EQ(buffer.nb_complete_steps(), 0); + ASSERT_THROW(buffer.get_rollout(), c10::Error); +} + +TEST_F(LiquidPpoRolloutBufferTest, LastAddedStepStaysPending) { + LiquidPpoRolloutBuffer buffer; + + buffer.add(make_step(make_state())); + ASSERT_EQ(buffer.nb_complete_steps(), 0); + + buffer.add(make_step(make_state())); + ASSERT_EQ(buffer.nb_complete_steps(), 1); +} + +TEST_F(LiquidPpoRolloutBufferTest, FinishEpisodeCompletesPendingStep) { + LiquidPpoRolloutBuffer buffer; + + buffer.add(make_step(make_state())); + buffer.add(make_step(make_state())); + buffer.finish_episode(make_state()); + + ASSERT_EQ(buffer.nb_complete_steps(), 2); +} + +TEST_F(LiquidPpoRolloutBufferTest, GetRolloutKeepsPendingStep) { + LiquidPpoRolloutBuffer buffer; + + buffer.add(make_step(make_state())); + buffer.add(make_step(make_state())); + buffer.add(make_step(make_state())); + + const auto rollout = buffer.get_rollout(); + ASSERT_EQ(rollout.rewards.size(0), 2); + + // the pending step stays and is closed by the next observation + ASSERT_EQ(buffer.nb_complete_steps(), 0); + buffer.add(make_step(make_state())); + ASSERT_EQ(buffer.nb_complete_steps(), 1); +} + +// ======================================================================== +// Rollout content +// ======================================================================== + +TEST_F(LiquidPpoRolloutBufferTest, RolloutShapes) { + LiquidPpoRolloutBuffer buffer; + + constexpr int nb_steps = 3; + for (int t = 0; t < nb_steps; t++) buffer.add(make_step(make_state())); + buffer.finish_episode(make_state()); + + const auto rollout = buffer.get_rollout(); + + const auto expected_vision = + std::vector{nb_steps, NB_TANKS, 3, VISION_SIZE, VISION_SIZE}; + ASSERT_EQ(rollout.states.vision.sizes().vec(), expected_vision); + + // the bootstrap state has no time dimension + ASSERT_EQ( + rollout.bootstrap_state.vision.sizes().vec(), + (std::vector{NB_TANKS, 3, VISION_SIZE, VISION_SIZE})); + + const auto expected_scalar = std::vector{nb_steps, NB_TANKS, 1}; + ASSERT_EQ(rollout.rewards.sizes().vec(), expected_scalar); + ASSERT_EQ(rollout.continuous_log_probs.sizes().vec(), expected_scalar); + ASSERT_EQ(rollout.discrete_log_probs.sizes().vec(), expected_scalar); + ASSERT_EQ(rollout.valids.sizes().vec(), expected_scalar); + + ASSERT_EQ( + rollout.actions.continuous_action.sizes().vec(), + (std::vector{nb_steps, NB_TANKS, NB_CONTINUOUS_ACTIONS})); + + // liquid extras: the per-step actor states and the episode-start flags + ASSERT_EQ( + rollout.actor_hiddens.sizes().vec(), + (std::vector{nb_steps, NB_TANKS, NEURON_NUMBER})); + ASSERT_EQ(rollout.episode_starts.sizes().vec(), (std::vector{nb_steps})); + ASSERT_EQ(rollout.episode_starts.dtype(), torch::kBool); +} + +TEST_F(LiquidPpoRolloutBufferTest, ActorHiddenRoundTrip) { + LiquidPpoRolloutBuffer buffer; + + const auto step_0 = make_step(make_state()); + const auto step_1 = make_step(make_state()); + + buffer.add(step_0); + buffer.add(step_1); + buffer.finish_episode(make_state()); + + const auto rollout = buffer.get_rollout(); + + ASSERT_TRUE(torch::allclose(rollout.actor_hiddens[0], step_0.actor_hidden)); + ASSERT_TRUE(torch::allclose(rollout.actor_hiddens[1], step_1.actor_hidden)); +} + +TEST_F(LiquidPpoRolloutBufferTest, EpisodeStartFlagsRoundTrip) { + LiquidPpoRolloutBuffer buffer; + + const auto zeros = torch::zeros({NB_TANKS, 1}); + buffer.add(make_step(make_state(), zeros, true)); + buffer.add(make_step(make_state(), zeros, false)); + buffer.add(make_step(make_state(), zeros, true)); + buffer.finish_episode(make_state()); + + const auto episode_starts = buffer.get_rollout().episode_starts; + + ASSERT_TRUE(episode_starts[0].item()); + ASSERT_FALSE(episode_starts[1].item()); + ASSERT_TRUE(episode_starts[2].item()); +} + +TEST_F(LiquidPpoRolloutBufferTest, BootstrapStateIsEpisodeFinalObservation) { + LiquidPpoRolloutBuffer buffer; + + const auto state_0 = make_state(); + const auto state_1 = make_state(); + const auto final_state = make_state(); + + buffer.add(make_step(state_0)); + buffer.add(make_step(state_1)); + buffer.finish_episode(final_state); + + const auto rollout = buffer.get_rollout(); + + ASSERT_EQ(rollout.rewards.size(0), 2); + ASSERT_TRUE(torch::allclose(rollout.bootstrap_state.vision, final_state.vision)); +} + +TEST_F(LiquidPpoRolloutBufferTest, BootstrapStateIsPendingObservationMidEpisode) { + LiquidPpoRolloutBuffer buffer; + + const auto state_0 = make_state(); + const auto state_1 = make_state(); + + buffer.add(make_step(state_0)); + buffer.add(make_step(state_1)); + + const auto rollout = buffer.get_rollout(); + + // only the first step is complete; the pending step's own observation closes it + ASSERT_EQ(rollout.rewards.size(0), 1); + ASSERT_TRUE(torch::allclose(rollout.bootstrap_state.vision, state_1.vision)); +} + +// ======================================================================== +// Validity mask +// ======================================================================== + +TEST_F(LiquidPpoRolloutBufferTest, TerminatedTankInvalidatesFollowingSteps) { + LiquidPpoRolloutBuffer buffer; + + // tank 0 dies at the first step + const auto done = torch::cat({torch::ones({1, 1}), torch::zeros({1, 1})}, 0); + + buffer.add(make_step(make_state(), done, false)); + buffer.add(make_step(make_state())); + buffer.add(make_step(make_state())); + buffer.finish_episode(make_state()); + + const auto valids = buffer.get_rollout().valids.squeeze(-1); + + // the dying transition itself is valid, the following ones are not for tank 0 + ASSERT_TRUE(valids[0][0].item()); + ASSERT_FALSE(valids[1][0].item()); + ASSERT_FALSE(valids[2][0].item()); + + // tank 1 stays valid the whole rollout + ASSERT_TRUE(valids[0][1].item()); + ASSERT_TRUE(valids[1][1].item()); + ASSERT_TRUE(valids[2][1].item()); +} + +TEST_F(LiquidPpoRolloutBufferTest, FinishEpisodeResetsTermination) { + LiquidPpoRolloutBuffer buffer; + + buffer.add(make_step(make_state(), torch::ones({NB_TANKS, 1}), false)); + buffer.finish_episode(make_state()); + + // new episode: every tank is alive again + buffer.add(make_step(make_state())); + buffer.finish_episode(make_state()); + + const auto valids = buffer.get_rollout().valids.squeeze(-1); + + ASSERT_TRUE(torch::all(valids[0]).item()); + ASSERT_TRUE(torch::all(valids[1]).item()); +} diff --git a/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_training.cpp b/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_training.cpp new file mode 100644 index 00000000..a97bc5ec --- /dev/null +++ b/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_training.cpp @@ -0,0 +1,179 @@ +// +// Created by samuel on 06/09/2026. +// + +#include + +using namespace arenai; +using namespace arenai::agent; + +std::unique_ptr +LiquidPpoTrainingTest::make_factory(const LiquidPpoTrainingTestConfig &cfg) const { + const LiquidPpoHyperParams params{ + .actor_learning_rate = 1e-3f, + .critic_learning_rate = 1e-3f, + .hidden_size_sensors = 8, + .vision_channels = {{3, 4}}, + .group_norm_nums = {2}, + .neuron_number = NEURON_NUMBER, + .unfolding_steps = UNFOLDING_STEPS, + .delta_t = DELTA_T, + .chunk_size = CHUNK_SIZE, + .metric_window_size = 10, + .gamma = 0.99f, + .gae_lambda = 0.95f, + .clip_epsilon = 0.2f, + .epochs = 2, + .rollout_size = ROLLOUT_SIZE, + .minibatch_size = MINIBATCH_SIZE}; + + return std::make_unique( + cfg.vision_height, cfg.vision_width, cfg.nb_sensors, cfg.nb_continuous_actions, + cfg.nb_discrete_actions, device, params); +} + +TorchState +LiquidPpoTrainingTest::make_state(const LiquidPpoTrainingTestConfig &cfg, const int nb_tanks) { + return { + .vision = torch::randint( + 0, 255, {nb_tanks, 3, cfg.vision_height, cfg.vision_width}, torch::kUInt8), + .proprioception = torch::randn({nb_tanks, cfg.nb_sensors})}; +} + +TEST_F(LiquidPpoTrainingTest, ActProducesValidOutput) { + constexpr LiquidPpoTrainingTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 3, + .nb_continuous_actions = 2, + .nb_discrete_actions = 3}; + const auto factory = make_factory(cfg); + + const auto [continuous_action, discrete_action] = + factory->get_agent()->act(make_state(cfg, 1), true); + + ASSERT_EQ(continuous_action.size(0), 1); + ASSERT_EQ(continuous_action.size(1), 2); + ASSERT_EQ(discrete_action.size(0), 1); + ASSERT_EQ(discrete_action.size(1), 3); + + ASSERT_TRUE(torch::all(torch::isfinite(continuous_action)).item()); + ASSERT_TRUE(torch::all(torch::isfinite(discrete_action)).item()); +} + +TEST_F(LiquidPpoTrainingTest, CountParametersPositive) { + constexpr LiquidPpoTrainingTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 3, + .nb_continuous_actions = 2, + .nb_discrete_actions = 3}; + const auto factory = make_factory(cfg); + + ASSERT_GT(factory->get_trainer()->count_parameters(), 0) + << "Agent should have a positive number of parameters"; +} + +TEST_F(LiquidPpoTrainingTest, HiddenStateAdvancesAndResetsAcrossEpisodes) { + // build the triad by hand to keep a handle on the rollout buffer + constexpr LiquidPpoTrainingTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 3, + .nb_continuous_actions = 2, + .nb_discrete_actions = 3}; + + constexpr int nb_tanks = 2; + const std::vector> vision_channels{{3, 4}}; + const std::vector group_norm_nums{2}; + + const auto actor = std::make_shared( + cfg.vision_height, cfg.vision_width, cfg.nb_sensors, cfg.nb_continuous_actions, + cfg.nb_discrete_actions, 8, vision_channels, group_norm_nums, NEURON_NUMBER, + UNFOLDING_STEPS, DELTA_T, 0.1f, 0.2f); + const auto hidden_state = std::make_shared(actor); + const auto rollout_buffer = std::make_shared(); + const auto collector = std::make_shared(rollout_buffer, hidden_state); + const auto agent = + std::make_shared(actor, hidden_state, device, collector); + + // first episode: two steps + agent->act(make_state(cfg, nb_tanks), true); + collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); + agent->act(make_state(cfg, nb_tanks), true); + collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); + collector->on_episode_end(make_state(cfg, nb_tanks)); + + // second episode: one step + agent->act(make_state(cfg, nb_tanks), true); + collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); + collector->on_episode_end(make_state(cfg, nb_tanks)); + + const auto rollout = rollout_buffer->get_rollout(); + + // within the episode, the liquid state advanced between the steps + ASSERT_FALSE(torch::allclose(rollout.actor_hiddens[1], rollout.actor_hiddens[0])); + + // episode boundaries: first step of each episode is flagged + ASSERT_TRUE(rollout.episode_starts[0].item()); + ASSERT_FALSE(rollout.episode_starts[1].item()); + ASSERT_TRUE(rollout.episode_starts[2].item()); +} + +TEST_F(LiquidPpoTrainingTest, TrainingUpdatesActorParameters) { + // build the triad by hand to keep a handle on the actor's parameters + constexpr LiquidPpoTrainingTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 3, + .nb_continuous_actions = 2, + .nb_discrete_actions = 3}; + + const std::vector> vision_channels{{3, 4}}; + const std::vector group_norm_nums{2}; + + const auto actor = std::make_shared( + cfg.vision_height, cfg.vision_width, cfg.nb_sensors, cfg.nb_continuous_actions, + cfg.nb_discrete_actions, 8, vision_channels, group_norm_nums, NEURON_NUMBER, + UNFOLDING_STEPS, DELTA_T, 0.1f, 0.2f); + const auto hidden_state = std::make_shared(actor); + const auto rollout_buffer = std::make_shared(); + const auto collector = std::make_shared(rollout_buffer, hidden_state); + const auto agent = + std::make_shared(actor, hidden_state, device, collector); + // target_kl = 0 : early stop disabled so every minibatch applies its update + const auto trainer = std::make_shared( + actor, rollout_buffer, cfg.vision_height, cfg.vision_width, cfg.nb_sensors, + cfg.nb_continuous_actions, cfg.nb_discrete_actions, 1e-3f, 1e-3f, 8, vision_channels, + group_norm_nums, NEURON_NUMBER, UNFOLDING_STEPS, DELTA_T, device, 10, 0.99f, 0.95f, 0.2f, + 0.f, 1.f, 0.25f, 0.98f, 2, ROLLOUT_SIZE, MINIBATCH_SIZE, CHUNK_SIZE); + + std::vector initial_parameters; + for (const auto ¶meter: actor->parameters()) + initial_parameters.push_back(parameter.detach().clone()); + + // env loop: act -> transition -> maybe train, one more step than the rollout + // horizon so that the batch is complete when the trainer checks + for (int t = 0; t < ROLLOUT_SIZE + 2; t++) { + constexpr int nb_tanks = 2; + + agent->act(make_state(cfg, nb_tanks), true); + collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); + trainer->step(); + } + + // the rollout has been consumed by the training + ASSERT_LT(rollout_buffer->nb_complete_steps(), static_cast(ROLLOUT_SIZE)); + + const auto parameters = actor->parameters(); + ASSERT_EQ(parameters.size(), initial_parameters.size()); + + bool any_changed = false; + for (size_t i = 0; i < parameters.size(); i++) + if (!torch::allclose(parameters[i], initial_parameters[i])) { + any_changed = true; + break; + } + + ASSERT_TRUE(any_changed) << "Training should update the actor's parameters"; +} From 14406f34789ca7158676f897b0e995bdb0e2915a Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 6 Sep 2026 16:15:12 +0200 Subject: [PATCH 09/57] less neuron for liquid networks --- arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h | 2 +- arenai_agent/src/networks/recurrent/liquid_actor.cpp | 2 +- arenai_agent/src/networks/recurrent/liquid_actor.h | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h index 0f39c235..6909265d 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h @@ -20,7 +20,7 @@ namespace arenai::agent { std::vector> vision_channels = {{3, 8}, {8, 16}, {16, 24}, {24, 32}, {32, 48}, {48, 64}}; std::vector group_norm_nums = {1, 2, 3, 4, 6, 8}; - int neuron_number = 512; + int neuron_number = 128; int unfolding_steps = 6; float delta_t = 1.f / 30.f; // TBPTT window: length of the contiguous sequences the updates run on diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.cpp b/arenai_agent/src/networks/recurrent/liquid_actor.cpp index f2e612bc..2588eefa 100644 --- a/arenai_agent/src/networks/recurrent/liquid_actor.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_actor.cpp @@ -103,7 +103,7 @@ namespace arenai::agent { .next_x = x}; } - torch::Tensor LiquidActor::initial_state(const int batch_size) { + torch::Tensor LiquidActor::initial_state(const int batch_size) const { return liquid->get_first_x(batch_size); } diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.h b/arenai_agent/src/networks/recurrent/liquid_actor.h index b2cd5376..546b6f75 100644 --- a/arenai_agent/src/networks/recurrent/liquid_actor.h +++ b/arenai_agent/src/networks/recurrent/liquid_actor.h @@ -44,7 +44,7 @@ namespace arenai::agent { LiquidActorOutput act_sequence( const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t); - torch::Tensor initial_state(int batch_size); + torch::Tensor initial_state(int batch_size) const; private: std::shared_ptr vision_encoder; From 7325bdc3f431d5d14176e06f477b58343e848052 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 6 Sep 2026 16:21:55 +0200 Subject: [PATCH 10/57] less neuron for liquid networks --- arenai_agent/src/main.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index 10aff1c4..1a651a21 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -38,7 +38,7 @@ int main(const int argc, char **argv) { parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(250.f); parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(1000.f); parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(1000.f); - parser.add_argument("--curriculum_delta").scan<'g', float>().default_value(25.f); + parser.add_argument("--curriculum_delta").scan<'g', float>().default_value(50.f); parser.add_argument("--curriculum_ratio_low").scan<'g', float>().default_value(0.02f); parser.add_argument("--curriculum_ratio_high").scan<'g', float>().default_value(0.04f); parser.add_argument("--curriculum_probe_window").scan<'i', int>().default_value(24); From 27ab1d25068431ffbf805665663e2517737803ff Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 6 Sep 2026 16:33:57 +0200 Subject: [PATCH 11/57] max spawn zone 2000m*2000m --- arenai_agent/src/main.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index 1a651a21..d166cb2d 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -36,8 +36,8 @@ int main(const int argc, char **argv) { parser.add_argument("--vision_width").scan<'i', int>().default_value(256); parser.add_argument("--initial_spawn_width").scan<'g', float>().default_value(250.f); parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(250.f); - parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(1000.f); - parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(1000.f); + parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(2000.f); + parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(2000.f); parser.add_argument("--curriculum_delta").scan<'g', float>().default_value(50.f); parser.add_argument("--curriculum_ratio_low").scan<'g', float>().default_value(0.02f); parser.add_argument("--curriculum_ratio_high").scan<'g', float>().default_value(0.04f); From 5e3f8dc81304c2507748ab292c31d9573aef661f Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 6 Sep 2026 16:45:20 +0200 Subject: [PATCH 12/57] init liquid weights + fix missing namespace for liquid modules --- .../networks/recurrent/liquid_recurrent.cpp | 9 +- .../src/networks/recurrent/liquid_recurrent.h | 88 ++++++++++--------- arenai_agent/src/networks_utils/init.cpp | 6 ++ arenai_agent/src/networks_utils/init.h | 2 + .../tests_networks/tests_liquid_recurrent.cpp | 3 + 5 files changed, 64 insertions(+), 44 deletions(-) diff --git a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp b/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp index f5a705c0..ee69b7f6 100644 --- a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp @@ -4,6 +4,11 @@ #include "./liquid_recurrent.h" +#include "../../networks_utils/init.h" + +using namespace arenai; +using namespace arenai::agent; + /* * Cell model */ @@ -19,7 +24,9 @@ CellModel::CellModel( torch::nn::Linear(torch::nn::LinearOptions(neuron_number, neuron_number).bias(false)))), biases(register_parameter("biases", torch::zeros({1, neuron_number}))), activation_function(activation_function) { - // TODO init here + + weights->apply(init_liquid_weights); + recurrent_weights->apply(init_liquid_weights); } torch::Tensor CellModel::forward(const torch::Tensor &x_t, const torch::Tensor &input_t) { diff --git a/arenai_agent/src/networks/recurrent/liquid_recurrent.h b/arenai_agent/src/networks/recurrent/liquid_recurrent.h index 7501cdba..037c98e9 100644 --- a/arenai_agent/src/networks/recurrent/liquid_recurrent.h +++ b/arenai_agent/src/networks/recurrent/liquid_recurrent.h @@ -7,63 +7,65 @@ #include -class CellModel : public torch::nn::Module { -public: - CellModel( - int neuron_number, int input_size, - const std::function &activation_function); +namespace arenai::agent { + class CellModel : public torch::nn::Module { + public: + CellModel( + int neuron_number, int input_size, + const std::function &activation_function); - torch::Tensor forward(const torch::Tensor &x_t, const torch::Tensor &input_t); + torch::Tensor forward(const torch::Tensor &x_t, const torch::Tensor &input_t); -private: - torch::nn::Linear weights; - torch::nn::Linear recurrent_weights; - torch::Tensor biases; + private: + torch::nn::Linear weights; + torch::nn::Linear recurrent_weights; + torch::Tensor biases; - std::function activation_function; -}; + std::function activation_function; + }; -class LiquidCell : public torch::nn::Module { -public: - LiquidCell( - int neuron_number, int input_size, int unfolding_steps, - const std::function &activation_function, - float delta_t); + class LiquidCell : public torch::nn::Module { + public: + LiquidCell( + int neuron_number, int input_size, int unfolding_steps, + const std::function &activation_function, + float delta_t); - torch::Tensor forward(const torch::Tensor &x_t, const torch::Tensor &input_t); + torch::Tensor forward(const torch::Tensor &x_t, const torch::Tensor &input_t); -private: - torch::Tensor a; - torch::Tensor raw_tau; + private: + torch::Tensor a; + torch::Tensor raw_tau; - std::shared_ptr f; + std::shared_ptr f; - int unfolding_steps; - float delta_t; + int unfolding_steps; + float delta_t; - torch::Tensor tau() const; -}; + torch::Tensor tau() const; + }; -class LiquidRecurrent : public torch::nn::Module { -public: - LiquidRecurrent( - int neuron_number, int input_size, int output_size, int unfolding_steps, - const std::function &activation_function, - float delta_t); + class LiquidRecurrent : public torch::nn::Module { + public: + LiquidRecurrent( + int neuron_number, int input_size, int output_size, int unfolding_steps, + const std::function &activation_function, + float delta_t); - torch::Tensor forward(const torch::Tensor &inputs); + torch::Tensor forward(const torch::Tensor &inputs); - // one recurrent step, the state is handled by the caller: (output, x_t_next) - std::tuple - forward_step(const torch::Tensor &x_t, const torch::Tensor &input_t); + // one recurrent step, the state is handled by the caller: (output, x_t_next) + std::tuple + forward_step(const torch::Tensor &x_t, const torch::Tensor &input_t); - torch::Tensor get_first_x(int batch_size); + torch::Tensor get_first_x(int batch_size); -private: - std::shared_ptr cell; - int neuron_number; + private: + std::shared_ptr cell; + int neuron_number; - torch::nn::Sequential to_output; -}; + torch::nn::Sequential to_output; + }; +}// namespace arenai::agent #endif//ARENAI_LIQUID_RECURRENT_H diff --git a/arenai_agent/src/networks_utils/init.cpp b/arenai_agent/src/networks_utils/init.cpp index eb04e53f..a15e3253 100644 --- a/arenai_agent/src/networks_utils/init.cpp +++ b/arenai_agent/src/networks_utils/init.cpp @@ -38,6 +38,12 @@ namespace arenai::agent { } } + void init_liquid_weights(torch::nn::Module &module) { + if (const auto *lin = module.as()) { + torch::nn::init::normal_(lin->weight, 0.f, 1e-2f); + } + } + void init_sigma_output_weights(torch::nn::Module &module, const float wanted_sigma) { const float min_log_sigma = std::log(SIGMA_MIN); const float max_log_sigma = std::log(SIGMA_MAX); diff --git a/arenai_agent/src/networks_utils/init.h b/arenai_agent/src/networks_utils/init.h index 5ef95f31..91d9c7af 100644 --- a/arenai_agent/src/networks_utils/init.h +++ b/arenai_agent/src/networks_utils/init.h @@ -11,6 +11,8 @@ namespace arenai::agent { void init_hidden_weights(torch::nn::Module &module); + void init_liquid_weights(torch::nn::Module &module); + void init_mu_output_weights(torch::nn::Module &module); void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma); void init_discrete_output_weights(torch::nn::Module &module, float initial_fire_probability); diff --git a/arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp b/arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp index 17e4cc49..08ba0195 100644 --- a/arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp @@ -6,6 +6,9 @@ #include +using namespace arenai; +using namespace arenai::agent; + namespace { constexpr float DELTA_T = 1.f; From 76dae650d0156cab466fa8ca996d51badfc9c639 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 6 Sep 2026 20:59:49 +0200 Subject: [PATCH 13/57] refactor agent factory --- arenai_agent/include/arenai_agent/factory.h | 24 +++-- .../include/arenai_agent/factory_set.h | 24 ----- arenai_agent/src/agents/factory.cpp | 91 ++++++++++++++++++- arenai_agent/src/agents/factory_set.cpp | 39 -------- .../src/core/agent_loading_checker.cpp | 6 +- arenai_desktop/src/game.cpp | 6 +- 6 files changed, 109 insertions(+), 81 deletions(-) delete mode 100644 arenai_agent/include/arenai_agent/factory_set.h delete mode 100644 arenai_agent/src/agents/factory_set.cpp diff --git a/arenai_agent/include/arenai_agent/factory.h b/arenai_agent/include/arenai_agent/factory.h index 75bdab64..74173c97 100644 --- a/arenai_agent/include/arenai_agent/factory.h +++ b/arenai_agent/include/arenai_agent/factory.h @@ -14,17 +14,18 @@ namespace arenai::agent { + enum AgentAlgorithm { SAC, PPO, PPO_LIQUID }; + class AgentFactory { public: - virtual ~AgentFactory() = default; - explicit AgentFactory(const std::map &arguments); std::shared_ptr get_agent( - const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_actions); + AgentAlgorithm algorithm, const int &vision_height, const int &vision_width, + const int &nb_sensors, const int &nb_continuous_actions, + const int &nb_discrete_actions); - protected: + private: template T get_value(const std::string &argument_name, T default_value) { if (!arguments.contains(argument_name)) return default_value; @@ -57,11 +58,18 @@ namespace arenai::agent { return parse_fn(value_as_string); } - virtual std::shared_ptr get_agent_impl( + std::shared_ptr create_sac_agent( const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_action) = 0; + const int &nb_continuous_actions, const int &nb_discrete_action); + + std::shared_ptr create_ppo_agent( + const int &vision_height, const int &vision_width, const int &nb_sensors, + const int &nb_continuous_actions, const int &nb_discrete_action); + + std::shared_ptr create_liquid_ppo_agent( + const int &vision_height, const int &vision_width, const int &nb_sensors, + const int &nb_continuous_actions, const int &nb_discrete_action); - private: std::map arguments; }; diff --git a/arenai_agent/include/arenai_agent/factory_set.h b/arenai_agent/include/arenai_agent/factory_set.h deleted file mode 100644 index f06d0cad..00000000 --- a/arenai_agent/include/arenai_agent/factory_set.h +++ /dev/null @@ -1,24 +0,0 @@ -// -// Created by samuel on 11/03/2026. -// - -#ifndef ARENAI_AGENT_HOST_FACTORY_SET_H -#define ARENAI_AGENT_HOST_FACTORY_SET_H - -#include "./factory.h" - -namespace arenai::agent { - - class ActorAgentFactory : public AgentFactory { - public: - explicit ActorAgentFactory(const std::map &arguments); - - protected: - std::shared_ptr get_agent_impl( - const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_action) override; - }; - -}// namespace arenai::agent - -#endif//ARENAI_AGENT_HOST_FACTORY_SET_H diff --git a/arenai_agent/src/agents/factory.cpp b/arenai_agent/src/agents/factory.cpp index 78d7d88c..93bf39ea 100644 --- a/arenai_agent/src/agents/factory.cpp +++ b/arenai_agent/src/agents/factory.cpp @@ -9,6 +9,11 @@ #include +#include "../utils/cli_parser.h" +#include "./ppo/ppo_agent.h" +#include "./ppo_liquid/liquid_ppo_agent.h" +#include "./sac/sac_agent.h" + using namespace arenai; using namespace arenai::agent; @@ -18,10 +23,27 @@ namespace arenai::agent { : arguments(arguments) {} std::shared_ptr AgentFactory::get_agent( - const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_actions) { - const auto agent = get_agent_impl( - vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions); + const AgentAlgorithm algorithm, const int &vision_height, const int &vision_width, + const int &nb_sensors, const int &nb_continuous_actions, const int &nb_discrete_actions) { + std::shared_ptr agent; + switch (algorithm) { + case SAC: + agent = create_sac_agent( + vision_height, vision_width, nb_sensors, nb_continuous_actions, + nb_discrete_actions); + break; + case PPO: + agent = create_ppo_agent( + vision_height, vision_width, nb_sensors, nb_continuous_actions, + nb_discrete_actions); + break; + case PPO_LIQUID: + agent = create_liquid_ppo_agent( + vision_height, vision_width, nb_sensors, nb_continuous_actions, + nb_discrete_actions); + break; + default: throw std::runtime_error("Unknown agent algorithm"); + } if (!arguments.empty()) { std::cerr << "Invalid argument(s) : " << std::get<0>(*arguments.begin()) << std::endl; @@ -31,4 +53,65 @@ namespace arenai::agent { return agent; } + std::shared_ptr AgentFactory::create_sac_agent( + const int &vision_height, const int &vision_width, const int &nb_sensors, + const int &nb_continuous_actions, const int &nb_discrete_action) { + return std::make_shared( + std::make_shared( + vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_action, + get_value("hidden_size_sensors", 128), + get_value("hidden_sizes", parse_cli_hidden_layer, {{1024, 512}}) + .layers, + get_value( + "vision_channels", parse_cli_vision_channels, + {{{3, 8}, {8, 16}, {16, 24}, {24, 32}, {32, 48}, {48, 64}}}) + .channels, + get_value( + "group_norm_nums", parse_cli_group_norms, {{{1, 2, 3, 4, 6, 8}}}) + .groups, + 0.f, 0.f), + get_value("cuda", false) ? torch::kCUDA : torch::kCPU); + } + + std::shared_ptr AgentFactory::create_ppo_agent( + const int &vision_height, const int &vision_width, const int &nb_sensors, + const int &nb_continuous_actions, const int &nb_discrete_action) { + return std::make_shared( + std::make_shared( + vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_action, + get_value("hidden_size_sensors", 128), + get_value("hidden_sizes", parse_cli_hidden_layer, {{1024, 512}}) + .layers, + get_value( + "vision_channels", parse_cli_vision_channels, + {{{3, 8}, {8, 16}, {16, 24}, {24, 32}, {32, 48}, {48, 64}}}) + .channels, + get_value( + "group_norm_nums", parse_cli_group_norms, {{{1, 2, 3, 4, 6, 8}}}) + .groups, + 0.f, 0.f), + get_value("cuda", false) ? torch::kCUDA : torch::kCPU); + } + + std::shared_ptr AgentFactory::create_liquid_ppo_agent( + const int &vision_height, const int &vision_width, const int &nb_sensors, + const int &nb_continuous_actions, const int &nb_discrete_action) { + const auto actor = std::make_shared( + vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_action, + get_value("hidden_size_sensors", 128), + get_value( + "vision_channels", parse_cli_vision_channels, + {{{3, 8}, {8, 16}, {16, 24}, {24, 32}, {32, 48}, {48, 64}}}) + .channels, + get_value( + "group_norm_nums", parse_cli_group_norms, {{{1, 2, 3, 4, 6, 8}}}) + .groups, + get_value("neuron_number", 128), get_value("unfolding_steps", 6), + get_value("delta_t", 1.f / 30.f), 0.f, 0.f); + + return std::make_shared( + actor, std::make_shared(actor), + get_value("cuda", false) ? torch::kCUDA : torch::kCPU); + } + }// namespace arenai::agent diff --git a/arenai_agent/src/agents/factory_set.cpp b/arenai_agent/src/agents/factory_set.cpp deleted file mode 100644 index 17e5f142..00000000 --- a/arenai_agent/src/agents/factory_set.cpp +++ /dev/null @@ -1,39 +0,0 @@ -// -// Created by samuel on 11/03/2026. -// - -#include - -#include "../utils/cli_parser.h" -#include "./ppo/ppo_agent.h" -#include "./sac/sac_agent.h" - -using namespace arenai; -using namespace arenai::agent; - -namespace arenai::agent { - - ActorAgentFactory::ActorAgentFactory(const std::map &arguments) - : AgentFactory(arguments) {} - - std::shared_ptr ActorAgentFactory::get_agent_impl( - const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_action) { - return std::make_shared( - std::make_shared( - vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_action, - get_value("hidden_size_sensors", 128), - get_value("hidden_sizes", parse_cli_hidden_layer, {{1024, 512}}) - .layers, - get_value( - "vision_channels", parse_cli_vision_channels, - {{{3, 8}, {8, 16}, {16, 24}, {24, 32}, {32, 48}, {48, 64}}}) - .channels, - get_value( - "group_norm_nums", parse_cli_group_norms, {{{1, 2, 3, 4, 6, 8}}}) - .groups, - 0.f, 0.f), - get_value("cuda", false) ? torch::kCUDA : torch::kCPU); - } - -}// namespace arenai::agent diff --git a/arenai_desktop/src/core/agent_loading_checker.cpp b/arenai_desktop/src/core/agent_loading_checker.cpp index ca069915..dadb5b05 100644 --- a/arenai_desktop/src/core/agent_loading_checker.cpp +++ b/arenai_desktop/src/core/agent_loading_checker.cpp @@ -4,7 +4,7 @@ #include "./agent_loading_checker.h" -#include +#include #include #include @@ -15,9 +15,9 @@ namespace arenai::desktop { try { const auto agent = - agent::ActorAgentFactory(model_options.hyper_parameters) + agent::AgentFactory(model_options.hyper_parameters) .get_agent( - model_options.vision_height, model_options.vision_width, + agent::PPO_LIQUID, model_options.vision_height, model_options.vision_width, model::ENEMY_PROPRIOCEPTION_SIZE, model::ENEMY_NB_CONTINUOUS_ACTION, model::ENEMY_NB_DISCRETE_ACTION); diff --git a/arenai_desktop/src/game.cpp b/arenai_desktop/src/game.cpp index 5960e7b3..7a30e435 100644 --- a/arenai_desktop/src/game.cpp +++ b/arenai_desktop/src/game.cpp @@ -7,7 +7,7 @@ #include #include -#include +#include #include #include #include @@ -30,9 +30,9 @@ namespace arenai::desktop { const auto window = graphics_backend->get_window(); const std::shared_ptr sac_agent = - agent::ActorAgentFactory(model_options.hyper_parameters) + agent::AgentFactory(model_options.hyper_parameters) .get_agent( - model_options.vision_height, model_options.vision_width, + agent::PPO_LIQUID, model_options.vision_height, model_options.vision_width, model::ENEMY_PROPRIOCEPTION_SIZE, model::ENEMY_NB_CONTINUOUS_ACTION, model::ENEMY_NB_DISCRETE_ACTION); From b0a71e629e2c69b425215f3a7c9e1b89d310375d Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sun, 6 Sep 2026 22:23:03 +0200 Subject: [PATCH 14/57] move saved networks files + add its config --- arenai_desktop/src/main.cpp | 2 +- .../trained_models/ppo_train_386/config.json | 43 ++++++++++++++++++ .../save_30}/actor.pt | Bin .../save_66}/actor.pt | Bin 4 files changed, 44 insertions(+), 1 deletion(-) create mode 100644 resources/trained_models/ppo_train_386/config.json rename resources/trained_models/{ppo_run_386_save_30 => ppo_train_386/save_30}/actor.pt (100%) rename resources/trained_models/{ppo_run_386_save_66 => ppo_train_386/save_66}/actor.pt (100%) diff --git a/arenai_desktop/src/main.cpp b/arenai_desktop/src/main.cpp index 8e44c5f6..188a3e54 100644 --- a/arenai_desktop/src/main.cpp +++ b/arenai_desktop/src/main.cpp @@ -85,7 +85,7 @@ int main(const int argc, char **argv) { {.vision_height = parser.get("--vision_height"), .vision_width = parser.get("--vision_width"), .hyper_parameters = hyper_params, - .state_dict_folder = resources_folder / "trained_models" / "ppo_run_386_save_66", + .state_dict_folder = resources_folder / "trained_models" / "ppo_train_386" / "save_66", .cuda = parser.get("--cuda")}); return 0; diff --git a/resources/trained_models/ppo_train_386/config.json b/resources/trained_models/ppo_train_386/config.json new file mode 100644 index 00000000..9179dcb9 --- /dev/null +++ b/resources/trained_models/ppo_train_386/config.json @@ -0,0 +1,43 @@ +{ + "agent": { + "actor_hidden_sizes": "[1024, 512]", + "actor_learning_rate": "0.0001", + "clip_epsilon": "0.2", + "continuous_target_entropy": "0.4", + "critic_hidden_sizes": "[1024, 512]", + "critic_learning_rate": "0.0003", + "discrete_target_entropy_factor": "0.2", + "epochs": "2", + "gae_lambda": "0.99", + "gamma": "0.997", + "grad_norm_max": "0.5", + "group_norm_nums": "[1, 2, 3, 4, 6, 8]", + "hidden_size_sensors": "128", + "initial_fire_proba": "0.4", + "initial_sigma": "0.5", + "metric_window_size": "256", + "minibatch_size": "1024", + "rollout_size": "900", + "target_kl": "0.05", + "vision_channels": "[(3, 8), (8, 16), (16, 24), (24, 32), (32, 48), (48, 64)]" + }, + "environment": { + "final_spawn_height": 500.0, + "final_spawn_width": 500.0, + "initial_spawn_height": 500.0, + "initial_spawn_width": 500.0, + "nb_tanks": 32, + "vision_height": 128, + "vision_num_threads": 16, + "vision_width": 256, + "wanted_frequency": 0.03333333507180214 + }, + "train": { + "cuda": true, + "max_episode_steps": 5400, + "nb_episodes": 6000, + "output_folder": "./outputs/train_386_ppo", + "resources_folder": "/home/samuel/CLionProjects/ArenAI/resources", + "save_every": 108000 + } +} diff --git a/resources/trained_models/ppo_run_386_save_30/actor.pt b/resources/trained_models/ppo_train_386/save_30/actor.pt similarity index 100% rename from resources/trained_models/ppo_run_386_save_30/actor.pt rename to resources/trained_models/ppo_train_386/save_30/actor.pt diff --git a/resources/trained_models/ppo_run_386_save_66/actor.pt b/resources/trained_models/ppo_train_386/save_66/actor.pt similarity index 100% rename from resources/trained_models/ppo_run_386_save_66/actor.pt rename to resources/trained_models/ppo_train_386/save_66/actor.pt From 249207c0dab53348536d4beed966491852cc8480 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 6 Sep 2026 22:23:44 +0200 Subject: [PATCH 15/57] refactor parameters menu # Conflicts: # arenai_desktop/src/main.cpp --- arenai_agent/CMakeLists.txt | 2 + arenai_agent/include/arenai_agent/factory.h | 59 +- arenai_agent/src/agents/factory.cpp | 63 +- .../src/core/agent_loading_checker.cpp | 65 +- .../src/core/agent_loading_checker.h | 21 +- arenai_desktop/src/core/user_preferences.cpp | 20 +- arenai_desktop/src/game.cpp | 50 +- arenai_desktop/src/game.h | 8 +- arenai_desktop/src/gui/menu.h | 41 +- arenai_desktop/src/gui/rml/gui.cpp | 466 +++++++ arenai_desktop/src/gui/rml/hud.cpp | 83 ++ arenai_desktop/src/gui/rml/hud.h | 46 + arenai_desktop/src/gui/rml/pages/ai_page.cpp | 161 +++ arenai_desktop/src/gui/rml/pages/ai_page.h | 71 ++ .../src/gui/rml/pages/controls_page.cpp | 379 ++++++ .../src/gui/rml/pages/controls_page.h | 101 ++ .../src/gui/rml/pages/graphics_page.cpp | 106 ++ .../src/gui/rml/pages/graphics_page.h | 53 + arenai_desktop/src/gui/rml_menu.cpp | 1066 ----------------- arenai_desktop/src/main.cpp | 49 +- resources/menu/ai.rml | 42 + resources/menu/controls.rml | 8 + resources/menu/graphics.rml | 8 + resources/menu/menu.rcss | 38 +- resources/menu/parameters.rml | 39 +- 25 files changed, 1765 insertions(+), 1280 deletions(-) create mode 100644 arenai_desktop/src/gui/rml/gui.cpp create mode 100644 arenai_desktop/src/gui/rml/hud.cpp create mode 100644 arenai_desktop/src/gui/rml/hud.h create mode 100644 arenai_desktop/src/gui/rml/pages/ai_page.cpp create mode 100644 arenai_desktop/src/gui/rml/pages/ai_page.h create mode 100644 arenai_desktop/src/gui/rml/pages/controls_page.cpp create mode 100644 arenai_desktop/src/gui/rml/pages/controls_page.h create mode 100644 arenai_desktop/src/gui/rml/pages/graphics_page.cpp create mode 100644 arenai_desktop/src/gui/rml/pages/graphics_page.h delete mode 100644 arenai_desktop/src/gui/rml_menu.cpp create mode 100644 resources/menu/ai.rml diff --git a/arenai_agent/CMakeLists.txt b/arenai_agent/CMakeLists.txt index 6ff658bf..8cb5dd3c 100644 --- a/arenai_agent/CMakeLists.txt +++ b/arenai_agent/CMakeLists.txt @@ -70,4 +70,6 @@ arenai_copy_runtime_dlls(arenai_agent_train TORCH) # Library add_library(arenai_agent SHARED ${ARENAI_AGENT_SRC}) target_link_libraries(arenai_agent PRIVATE ${ARENAI_AGENT_LINK_LIBS} "${stb_SOURCE_DIR}") +# nlohmann::json appears in the public factory.h contract +target_link_libraries(arenai_agent PUBLIC nlohmann_json::nlohmann_json) target_include_directories(arenai_agent PUBLIC "${CMAKE_CURRENT_SOURCE_DIR}/include") diff --git a/arenai_agent/include/arenai_agent/factory.h b/arenai_agent/include/arenai_agent/factory.h index 74173c97..c1f198aa 100644 --- a/arenai_agent/include/arenai_agent/factory.h +++ b/arenai_agent/include/arenai_agent/factory.h @@ -6,10 +6,11 @@ #define ARENAI_AGENT_HOST_FACTORY_H #include -#include #include #include +#include + #include "./agent.h" namespace arenai::agent { @@ -18,59 +19,67 @@ namespace arenai::agent { class AgentFactory { public: - explicit AgentFactory(const std::map &arguments); + // config: the content of a training run's config.json — the network + // hyper-parameters come from its "agent" section, the vision size and + // the control frequency from its "environment" section + explicit AgentFactory(const nlohmann::json &config); std::shared_ptr get_agent( - AgentAlgorithm algorithm, const int &vision_height, const int &vision_width, - const int &nb_sensors, const int &nb_continuous_actions, - const int &nb_discrete_actions); + AgentAlgorithm algorithm, const int &nb_sensors, const int &nb_continuous_actions, + const int &nb_discrete_actions, bool cuda); + + int get_vision_height() const; + int get_vision_width() const; + float get_wanted_frequency() const; private: template T get_value(const std::string &argument_name, T default_value) { - if (!arguments.contains(argument_name)) return default_value; + if (!agent_arguments.contains(argument_name)) return default_value; + + const auto &value = agent_arguments[argument_name]; + if (!value.is_string()) return value.get(); - const std::string value_as_string = arguments[argument_name]; + // runs dumped before the json migration stored every value as the + // CLI string it came from + const auto value_as_string = value.get(); std::stringstream ss(value_as_string); - T value; - ss >> value; + T parsed_value; + ss >> parsed_value; if (ss.fail() || !ss.eof()) throw std::runtime_error(std::format( R"(Wrong value for "{}" : "{}", example : "{}")", argument_name, value_as_string, default_value)); - arguments.erase(arguments.find(argument_name)); - - return value; + return parsed_value; } template T get_value( const std::string &argument_name, const std::function &parse_fn, T default_value) { - if (!arguments.contains(argument_name)) return default_value; - - const std::string value_as_string = arguments[argument_name]; - - arguments.erase(arguments.find(argument_name)); + if (!agent_arguments.contains(argument_name)) return default_value; - return parse_fn(value_as_string); + return parse_fn(agent_arguments[argument_name].get()); } std::shared_ptr create_sac_agent( - const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_action); + const int &nb_sensors, const int &nb_continuous_actions, const int &nb_discrete_action, + bool cuda); std::shared_ptr create_ppo_agent( - const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_action); + const int &nb_sensors, const int &nb_continuous_actions, const int &nb_discrete_action, + bool cuda); std::shared_ptr create_liquid_ppo_agent( - const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_action); + const int &nb_sensors, const int &nb_continuous_actions, const int &nb_discrete_action, + bool cuda); - std::map arguments; + nlohmann::json agent_arguments; + int vision_height; + int vision_width; + float wanted_frequency; }; }// namespace arenai::agent diff --git a/arenai_agent/src/agents/factory.cpp b/arenai_agent/src/agents/factory.cpp index 93bf39ea..798d068b 100644 --- a/arenai_agent/src/agents/factory.cpp +++ b/arenai_agent/src/agents/factory.cpp @@ -2,9 +2,6 @@ // Created by samuel on 22/01/2026. // -#include -#include -#include #include #include @@ -19,43 +16,36 @@ using namespace arenai::agent; namespace arenai::agent { - AgentFactory::AgentFactory(const std::map &arguments) - : arguments(arguments) {} + AgentFactory::AgentFactory(const nlohmann::json &config) + : agent_arguments(config.at("agent")), + vision_height(config.at("environment").at("vision_height").get()), + vision_width(config.at("environment").at("vision_width").get()), + wanted_frequency(config.at("environment").at("wanted_frequency").get()) {} + + int AgentFactory::get_vision_height() const { return vision_height; } + int AgentFactory::get_vision_width() const { return vision_width; } + float AgentFactory::get_wanted_frequency() const { return wanted_frequency; } std::shared_ptr AgentFactory::get_agent( - const AgentAlgorithm algorithm, const int &vision_height, const int &vision_width, - const int &nb_sensors, const int &nb_continuous_actions, const int &nb_discrete_actions) { - std::shared_ptr agent; + const AgentAlgorithm algorithm, const int &nb_sensors, const int &nb_continuous_actions, + const int &nb_discrete_actions, const bool cuda) { switch (algorithm) { case SAC: - agent = create_sac_agent( - vision_height, vision_width, nb_sensors, nb_continuous_actions, - nb_discrete_actions); - break; + return create_sac_agent( + nb_sensors, nb_continuous_actions, nb_discrete_actions, cuda); case PPO: - agent = create_ppo_agent( - vision_height, vision_width, nb_sensors, nb_continuous_actions, - nb_discrete_actions); - break; + return create_ppo_agent( + nb_sensors, nb_continuous_actions, nb_discrete_actions, cuda); case PPO_LIQUID: - agent = create_liquid_ppo_agent( - vision_height, vision_width, nb_sensors, nb_continuous_actions, - nb_discrete_actions); - break; + return create_liquid_ppo_agent( + nb_sensors, nb_continuous_actions, nb_discrete_actions, cuda); default: throw std::runtime_error("Unknown agent algorithm"); } - - if (!arguments.empty()) { - std::cerr << "Invalid argument(s) : " << std::get<0>(*arguments.begin()) << std::endl; - throw std::runtime_error("Invalid argument(s)"); - } - - return agent; } std::shared_ptr AgentFactory::create_sac_agent( - const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_action) { + const int &nb_sensors, const int &nb_continuous_actions, const int &nb_discrete_action, + const bool cuda) { return std::make_shared( std::make_shared( vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_action, @@ -70,12 +60,12 @@ namespace arenai::agent { "group_norm_nums", parse_cli_group_norms, {{{1, 2, 3, 4, 6, 8}}}) .groups, 0.f, 0.f), - get_value("cuda", false) ? torch::kCUDA : torch::kCPU); + cuda ? torch::kCUDA : torch::kCPU); } std::shared_ptr AgentFactory::create_ppo_agent( - const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_action) { + const int &nb_sensors, const int &nb_continuous_actions, const int &nb_discrete_action, + const bool cuda) { return std::make_shared( std::make_shared( vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_action, @@ -90,12 +80,12 @@ namespace arenai::agent { "group_norm_nums", parse_cli_group_norms, {{{1, 2, 3, 4, 6, 8}}}) .groups, 0.f, 0.f), - get_value("cuda", false) ? torch::kCUDA : torch::kCPU); + cuda ? torch::kCUDA : torch::kCPU); } std::shared_ptr AgentFactory::create_liquid_ppo_agent( - const int &vision_height, const int &vision_width, const int &nb_sensors, - const int &nb_continuous_actions, const int &nb_discrete_action) { + const int &nb_sensors, const int &nb_continuous_actions, const int &nb_discrete_action, + const bool cuda) { const auto actor = std::make_shared( vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_action, get_value("hidden_size_sensors", 128), @@ -110,8 +100,7 @@ namespace arenai::agent { get_value("delta_t", 1.f / 30.f), 0.f, 0.f); return std::make_shared( - actor, std::make_shared(actor), - get_value("cuda", false) ? torch::kCUDA : torch::kCPU); + actor, std::make_shared(actor), cuda ? torch::kCUDA : torch::kCPU); } }// namespace arenai::agent diff --git a/arenai_desktop/src/core/agent_loading_checker.cpp b/arenai_desktop/src/core/agent_loading_checker.cpp index dadb5b05..32fdb38b 100644 --- a/arenai_desktop/src/core/agent_loading_checker.cpp +++ b/arenai_desktop/src/core/agent_loading_checker.cpp @@ -4,32 +4,65 @@ #include "./agent_loading_checker.h" -#include +#include + +#include + #include -#include namespace arenai::desktop { - std::optional - check_agent_folder(const ModelOptions &model_options, const std::filesystem::path &folder) { + agent::AgentAlgorithm to_agent_algorithm(const gui::AiAlgorithm algorithm) { + switch (algorithm) { + case gui::AiAlgorithm::Sac: return agent::SAC; + case gui::AiAlgorithm::Ppo: return agent::PPO; + default: return agent::PPO_LIQUID; + } + } + + std::optional + resolve_agent_config(const gui::AgentSelection &selection) { + if (!selection.config.empty()) return selection.config; + + for (const auto &candidate: + {selection.folder / "config.json", selection.folder.parent_path() / "config.json"}) + if (std::filesystem::is_regular_file(candidate)) return candidate; + + return std::nullopt; + } + + std::optional check_agent(const gui::AgentSelection &selection) { + const auto config_path = resolve_agent_config(selection); + if (!config_path) + return "config.json not found in " + selection.folder.string() + " or its parent"; + try { + std::ifstream config_stream(*config_path); + if (!config_stream.is_open()) + throw std::runtime_error("cannot open " + config_path->string()); + const auto config = nlohmann::json::parse(config_stream); - const auto agent = - agent::AgentFactory(model_options.hyper_parameters) - .get_agent( - agent::PPO_LIQUID, model_options.vision_height, model_options.vision_width, - model::ENEMY_PROPRIOCEPTION_SIZE, model::ENEMY_NB_CONTINUOUS_ACTION, - model::ENEMY_NB_DISCRETE_ACTION); + agent::AgentFactory factory(config); // stays on CPU: the point is only to prove the state dicts load - agent->load(folder); + // and the networks answer a forward pass + const auto agent = factory.get_agent( + to_agent_algorithm(selection.algorithm), model::ENEMY_PROPRIOCEPTION_SIZE, + model::ENEMY_NB_CONTINUOUS_ACTION, model::ENEMY_NB_DISCRETE_ACTION, false); + + agent->load(selection.folder); + + const core::State blank_state = { + .vision = + {.pixels = std::vector( + static_cast(3) * factory.get_vision_height() + * factory.get_vision_width(), + 0)}, + .proprioception = std::vector(model::ENEMY_PROPRIOCEPTION_SIZE, 0.f)}; + agent->act({blank_state}, factory.get_vision_height(), factory.get_vision_width()); return std::nullopt; - } catch (utils::FileDoesNotExistException &e) { - return "Missing file: " + e.missing_file().filename().string(); - } catch (utils::ModelLoadException &e) { - return "Error while loading file: " + e.wrong_state_dict_file().filename().string(); - } catch (const std::exception &_) { return "Unknow error while loading Model"; } + } catch (const std::exception &e) { return e.what(); } } }// namespace arenai::desktop diff --git a/arenai_desktop/src/core/agent_loading_checker.h b/arenai_desktop/src/core/agent_loading_checker.h index 3cf6560f..752440b7 100644 --- a/arenai_desktop/src/core/agent_loading_checker.h +++ b/arenai_desktop/src/core/agent_loading_checker.h @@ -9,15 +9,24 @@ #include #include -#include "../game.h" +#include + +#include "../gui/menu.h" namespace arenai::desktop { - // Dry-run load of the SAC agent: builds the networks and loads every - // state dict on CPU, then throws the agent away. Returns std::nullopt - // when the folder is a valid checkpoint, else the message to display. - std::optional - check_agent_folder(const ModelOptions &model_options, const std::filesystem::path &folder); + agent::AgentAlgorithm to_agent_algorithm(gui::AiAlgorithm algorithm); + + // the config.json a selection uses: the explicitly picked file when set, + // else the one sitting in the state-dict folder, else in its parent (the + // training layout: train_NNN/config.json next to train_NNN/save_K/) + std::optional resolve_agent_config(const gui::AgentSelection &selection); + + // Dry-run load of the selected agent: builds the networks from the + // config.json, loads every state dict on CPU and answers one forward pass + // on a blank state, then throws the agent away. Returns std::nullopt when + // the selection is a working model, else the exception message to display. + std::optional check_agent(const gui::AgentSelection &selection); }// namespace arenai::desktop diff --git a/arenai_desktop/src/core/user_preferences.cpp b/arenai_desktop/src/core/user_preferences.cpp index 6d0e0a3e..16bbad94 100644 --- a/arenai_desktop/src/core/user_preferences.cpp +++ b/arenai_desktop/src/core/user_preferences.cpp @@ -182,11 +182,19 @@ namespace arenai::desktop { bindings.is_object()) load_bindings(bindings, settings.bindings); - // a stale folder (moved, deleted, unplugged drive) falls back to + // a stale path (moved, deleted, unplugged drive) falls back to // the default so the menu never starts on an unplayable selection - if (const std::filesystem::path sac_folder = json.value("sac_folder", std::string()); - !sac_folder.empty() && std::filesystem::is_directory(sac_folder)) - settings.sac_folder = sac_folder; + if (const std::filesystem::path agent_folder = + json.value("agent_folder", std::string()); + !agent_folder.empty() && std::filesystem::is_directory(agent_folder)) + settings.agent_folder = agent_folder; + if (const std::filesystem::path agent_config = + json.value("agent_config", std::string()); + !agent_config.empty() && std::filesystem::is_regular_file(agent_config)) + settings.agent_config = agent_config; + if (const auto algorithm = + gui::ai_algorithm_from_string(json.value("algorithm", std::string()))) + settings.agent_algorithm = *algorithm; } catch (const std::exception &e) { std::cerr << "Cannot load preferences " << path << ": " << e.what() << std::endl; return defaults; @@ -210,7 +218,9 @@ namespace arenai::desktop { {"window_gpu", settings.window_gpu}, {"vision_gpu", settings.vision_gpu}, {"bindings", bindings_to_json(settings.bindings)}, - {"sac_folder", settings.sac_folder.string()}, + {"agent_folder", settings.agent_folder.string()}, + {"agent_config", settings.agent_config.string()}, + {"algorithm", gui::to_string(settings.agent_algorithm)}, }; std::filesystem::create_directories(path.parent_path()); diff --git a/arenai_desktop/src/game.cpp b/arenai_desktop/src/game.cpp index 7a30e435..3f261199 100644 --- a/arenai_desktop/src/game.cpp +++ b/arenai_desktop/src/game.cpp @@ -5,8 +5,11 @@ #include "./game.h" #include +#include #include +#include + #include #include #include @@ -29,14 +32,30 @@ namespace arenai::desktop { const std::unique_ptr &gui) { const auto window = graphics_backend->get_window(); - const std::shared_ptr sac_agent = - agent::AgentFactory(model_options.hyper_parameters) - .get_agent( - agent::PPO_LIQUID, model_options.vision_height, model_options.vision_width, - model::ENEMY_PROPRIOCEPTION_SIZE, model::ENEMY_NB_CONTINUOUS_ACTION, - model::ENEMY_NB_DISCRETE_ACTION); + // the menu only lets Play through once check_agent() validated the + // selection, so the config.json is there and the load succeeds + const gui::AgentSelection selection = { + .config = settings.agent_config, + .folder = settings.agent_folder, + .algorithm = settings.agent_algorithm}; + const auto config_path = resolve_agent_config(selection); + if (!config_path) + throw std::runtime_error( + "config.json not found in " + selection.folder.string() + " or its parent"); + + std::ifstream config_stream(*config_path); + const auto config = nlohmann::json::parse(config_stream); + + agent::AgentFactory factory(config); + const int vision_height = factory.get_vision_height(); + const int vision_width = factory.get_vision_width(); + const float wanted_frequency = factory.get_wanted_frequency(); + + const std::shared_ptr enemy_agent = factory.get_agent( + to_agent_algorithm(settings.agent_algorithm), model::ENEMY_PROPRIOCEPTION_SIZE, + model::ENEMY_NB_CONTINUOUS_ACTION, model::ENEMY_NB_DISCRETE_ACTION, model_options.cuda); - sac_agent->load(settings.sac_folder); + enemy_agent->load(settings.agent_folder); // route the pad input to the configured device when it is connected // (also covers runs that skip the menu, e.g. ARENAI_DEBUG_AUTOPLAY) @@ -49,8 +68,8 @@ namespace arenai::desktop { } const auto env = std::make_shared( - game_options.resources_folder, graphics_backend, settings, model_options.vision_height, - model_options.vision_width, game_options.wanted_frequency); + game_options.resources_folder, graphics_backend, settings, vision_height, vision_width, + wanted_frequency); auto states = env->reset( static_cast(settings.spawn_side), static_cast(settings.spawn_side)); @@ -98,7 +117,7 @@ namespace arenai::desktop { auto outcome = InGameOutcome::ExitGame; const auto frame_dt = - std::chrono::milliseconds(static_cast(game_options.wanted_frequency * 1000.f)); + std::chrono::milliseconds(static_cast(wanted_frequency * 1000.f)); while (!window->should_close()) { window->poll_events(); @@ -129,10 +148,9 @@ namespace arenai::desktop { auto last_time = std::chrono::steady_clock::now(); - const auto action = - sac_agent->act(states, model_options.vision_height, model_options.vision_width); + const auto action = enemy_agent->act(states, vision_height, vision_width); - const auto steps = env->step(game_options.wanted_frequency, action); + const auto steps = env->step(wanted_frequency, action); if (const auto [hits, kills] = env->consume_player_hits(); kills > 0) gui->notify_hit(gui::HitKind::Kill); @@ -172,7 +190,7 @@ namespace arenai::desktop { // loaded before the backend: the window GPU choice only applies at // device creation, i.e. here const auto initial_settings = - load_preferences({.sac_folder = model_options.state_dict_folder}); + load_preferences({.agent_folder = model_options.state_dict_folder}); const std::shared_ptr graphics_backend = view::make_glfw_vulkan_backend( game_options.window_width, game_options.window_height, "ArenAI", @@ -187,9 +205,7 @@ namespace arenai::desktop { const auto gui = gui::make_gui( graphics_backend, asset_reader, initial_settings, view::list_vulkan_gpus(), game_options.window_width, game_options.window_height, - [&model_options](const std::filesystem::path &folder) { - return check_agent_folder(model_options, folder); - }); + [](const gui::AgentSelection &selection) { return check_agent(selection); }); window->set_resize_callback( [&gui](const int width, const int height) { gui->on_window_resized(width, height); }); diff --git a/arenai_desktop/src/game.h b/arenai_desktop/src/game.h index 96eacc5f..99e353d5 100644 --- a/arenai_desktop/src/game.h +++ b/arenai_desktop/src/game.h @@ -6,23 +6,19 @@ #define ARENAI_DESKTOP_GAME_H #include -#include -#include #include "./gui/menu.h" namespace arenai::desktop { + // everything else about the model (vision size, control frequency, + // hyper-parameters) comes from the selected training run's config.json struct ModelOptions { - int vision_height; - int vision_width; - std::map hyper_parameters; std::filesystem::path state_dict_folder; bool cuda; }; struct GameOptions { - float wanted_frequency; int window_width; int window_height; std::filesystem::path resources_folder; diff --git a/arenai_desktop/src/gui/menu.h b/arenai_desktop/src/gui/menu.h index 7953c6cc..bfdaaecf 100644 --- a/arenai_desktop/src/gui/menu.h +++ b/arenai_desktop/src/gui/menu.h @@ -24,7 +24,7 @@ // The gui/ folder is a hexagon of its own: this header is its only public // port, and it exposes no RmlUi type — the library stays an implementation -// detail of rml_menu.cpp and the rml/ subfolder, exactly like GL stays +// detail of the rml/ subfolder, exactly like GL stays // inside arenai_view. namespace arenai::desktop::gui { @@ -61,6 +61,25 @@ namespace arenai::desktop::gui { } } + // the RL algorithms the enemy agent can be built from; mirrors + // agent::AgentAlgorithm without leaking arenai_agent into the gui port + enum class AiAlgorithm { Sac, Ppo, PpoLiquid }; + + // canonical names, shared by the JSON preferences and the menu bindings + constexpr const char *to_string(const AiAlgorithm algorithm) { + switch (algorithm) { + case AiAlgorithm::Sac: return "sac"; + case AiAlgorithm::Ppo: return "ppo"; + default: return "ppo_liquid"; + } + } + + constexpr std::optional ai_algorithm_from_string(std::string_view name) { + for (const auto algorithm: {AiAlgorithm::Sac, AiAlgorithm::Ppo, AiAlgorithm::PpoLiquid}) + if (name == to_string(algorithm)) return algorithm; + return std::nullopt; + } + // what the player can tune in the menu before launching a game struct GameSettings { int nb_tanks = 16; @@ -77,7 +96,12 @@ namespace arenai::desktop::gui { std::string window_gpu; std::string vision_gpu; - std::filesystem::path sac_folder; + // the trained enemy agent: its state-dict folder, the config.json of + // its training run (empty = looked up next to the state dicts) and + // the algorithm to rebuild the networks with + std::filesystem::path agent_folder; + std::filesystem::path agent_config; + AiAlgorithm agent_algorithm = AiAlgorithm::PpoLiquid; }; enum class MenuOutcome { Play, Quit }; @@ -115,14 +139,21 @@ namespace arenai::desktop::gui { virtual void on_window_resized(int width, int height) = 0; }; - using SacFolderValidator = - std::function(const std::filesystem::path &)>; + // what the AI page hands to the dry-run loader: nullopt = the model + // loaded and answered a forward pass, else the message to display + struct AgentSelection { + std::filesystem::path config;// empty = auto-resolved config.json + std::filesystem::path folder; + AiAlgorithm algorithm; + }; + + using AgentValidator = std::function(const AgentSelection &)>; std::unique_ptr make_gui( const std::shared_ptr &backend, const std::shared_ptr &asset_reader, const GameSettings &initial_settings, const std::vector &gpus, - int window_width, int window_height, SacFolderValidator sac_validator); + int window_width, int window_height, AgentValidator agent_validator); }// namespace arenai::desktop::gui diff --git a/arenai_desktop/src/gui/rml/gui.cpp b/arenai_desktop/src/gui/rml/gui.cpp new file mode 100644 index 00000000..1f506883 --- /dev/null +++ b/arenai_desktop/src/gui/rml/gui.cpp @@ -0,0 +1,466 @@ +// +// Created by samuel on 17/07/2026. +// + +#include +#include +#include +#include + +#include + +#include +#include + +#include "../menu.h" +#include "./adapters.h" +#include "./cursor_ring.h" +#include "./damage_arc.h" +#include "./hit_marker.h" +#include "./hud.h" +#include "./input.h" +#include "./pages/ai_page.h" +#include "./pages/controls_page.h" +#include "./pages/graphics_page.h" +#include "./reticle.h" + +namespace arenai::desktop::gui { + + namespace { + + // Orchestrates the RmlUi machinery: library lifecycle, the loaded + // documents and the navigation between them, the main-menu loop and + // the pause / game-over popups. Everything page-specific lives in the + // pages/ classes, the in-game overlay in Hud. + class RmlGui final : public AbstractGui { + public: + RmlGui( + const std::shared_ptr &backend, + const std::shared_ptr &asset_reader, + const GameSettings &initial_settings, const std::vector &gpus, + const int window_width, const int window_height, AgentValidator agent_validator) + : backend_(backend), window_(backend->get_window()), settings_(initial_settings), + width_(window_width), height_(window_height), file_interface_(asset_reader), + graphics_page_(settings_, window_, gpus), controls_page_(settings_, window_), + ai_page_(settings_, std::move(agent_validator)) { + Rml::SetSystemInterface(&system_interface_); + Rml::SetFileInterface(&file_interface_); + Rml::SetRenderInterface(&backend_->ui_render_interface()); + Rml::Initialise(); + + // menu.rcss draws the slider knob's detached cursor ring with + // this gui-local decorator. Built only now: its property + // registration needs the style-sheet specification that + // Rml::Initialise() just created, so it cannot be a plain + // member (members are constructed before this body runs). + cursor_ring_instancer_ = std::make_unique(); + Rml::Factory::RegisterDecoratorInstancer( + "cursor-ring", cursor_ring_instancer_.get()); + hit_marker_instancer_ = std::make_unique(); + Rml::Factory::RegisterDecoratorInstancer("hit-marker", hit_marker_instancer_.get()); + reticle_instancer_ = std::make_unique(); + Rml::Factory::RegisterDecoratorInstancer("reticle", reticle_instancer_.get()); + damage_arc_instancer_ = std::make_unique(); + Rml::Factory::RegisterDecoratorInstancer("damage-arc", damage_arc_instancer_.get()); + + load_fonts(asset_reader); + + context_ = Rml::CreateContext("menu", Rml::Vector2i(width_, height_)); + if (!context_) throw std::runtime_error("RmlUi context creation failed"); + update_dp_ratio(); + + bind_data_model(); + ai_page_.refresh_explorer(); + + main_document_ = context_->LoadDocument("menu/main_menu.rml"); + params_document_ = context_->LoadDocument("menu/parameters.rml"); + controls_document_ = context_->LoadDocument("menu/controls.rml"); + graphics_document_ = context_->LoadDocument("menu/graphics.rml"); + ai_document_ = context_->LoadDocument("menu/ai.rml"); + pause_document_ = context_->LoadDocument("menu/pause.rml"); + game_over_document_ = context_->LoadDocument("menu/game_over.rml"); + hud_document_ = context_->LoadDocument("menu/hud.rml"); + if (!main_document_ || !params_document_ || !controls_document_ + || !graphics_document_ || !ai_document_ || !pause_document_ + || !game_over_document_ || !hud_document_) + throw std::runtime_error("RmlUi menu documents failed to load"); + + hud_ = std::make_unique(*hud_document_); + hud_document_->Show(Rml::ModalFlag::None, Rml::FocusFlag::None); + + // D-pad bridge across the file explorer's scroll container + ai_page_.attach(*ai_document_); + + input_adapter_ = std::make_shared( + context_, + [this] { + // Escape / gamepad B back out of the controls and + // parameters screens; while paused B resumes the game + // (the application intercepts Escape itself before + // this adapter); the game-over popup cannot be backed + // out of + if (game_over_document_->IsVisible()) return; + if (pause_document_->IsVisible()) + pending_pause_action_ = PauseAction::Continue; + else if (controls_document_->IsVisible()) close_controls(); + else if (graphics_document_->IsVisible()) close_graphics(); + else if (ai_document_->IsVisible()) close_ai(); + else if (params_document_->IsVisible()) close_params(); + }, + [this](const bool gamepad) { + // menu.rcss shows the :focus highlight only under + // .gamepad-nav, so the mouse hover and the gamepad + // cursor are never visible together + for (auto *document: + {main_document_, params_document_, controls_document_, + graphics_document_, ai_document_, pause_document_, + game_over_document_}) + document->SetClass("gamepad-nav", gamepad); + }); + controls_page_.set_input_adapter(input_adapter_); + + // route the pad input to the pad persisted from the previous + // session, when it is connected + controls_page_.on_open(); + } + + MenuOutcome run_main_menu() override { + play_clicked_ = false; + quit_clicked_ = false; + + window_->set_keyboard_callback(input_adapter_); + window_->set_gamepad_callback(input_adapter_); + window_->set_cursor_mode(controller::CursorMode::Normal); + main_document_->Show(); + + while (!window_->should_close() && !play_clicked_ && !quit_clicked_) { + window_->poll_events(); + + controls_page_.update(controls_document_->IsVisible()); + + context_->Update(); + + ai_page_.update_after_context(); + + backend_->begin_ui_frame(width_, height_); + context_->Render(); + backend_->end_ui_frame(); + backend_->present(); + } + + main_document_->Hide(); + params_document_->Hide(); + graphics_document_->Hide(); + ai_document_->Hide(); + controls_page_.on_close(); + controls_document_->Hide(); + window_->set_keyboard_callback(nullptr); + window_->set_gamepad_callback(nullptr); + + return play_clicked_ ? MenuOutcome::Play : MenuOutcome::Quit; + } + + GameSettings settings() const override { return settings_; } + + void open_pause(const int score) override { + pending_pause_action_ = PauseAction::None; + score_ = score; + model_handle_.DirtyVariable("score"); + pause_document_->Show(); + } + + void close_pause() override { pause_document_->Hide(); } + + void open_game_over(const int score) override { + pending_pause_action_ = PauseAction::None; + score_ = score; + model_handle_.DirtyVariable("score"); + game_over_document_->Show(); + } + + void close_game_over() override { game_over_document_->Hide(); } + + void render_pause_overlay() override { + context_->Update(); + + backend_->begin_ui_overlay(width_, height_); + context_->Render(); + backend_->end_ui_frame(); + } + + PauseAction poll_pause_action() override { + return std::exchange(pending_pause_action_, PauseAction::None); + } + + void notify_hit(const HitKind kind) override { hud_->notify_hit(kind); } + + void notify_damage(const float screen_angle) override { + hud_->notify_damage(screen_angle); + } + + void set_aim_point(const std::optional normalized) override { + hud_->set_aim_point(normalized, width_, height_); + } + + void render_hud_overlay() override { + context_->Update(); + + backend_->begin_ui_overlay(width_, height_); + context_->Render(); + backend_->end_ui_frame(); + } + + std::shared_ptr pause_input() override { + return input_adapter_; + } + + std::shared_ptr pause_gamepad_input() override { + return input_adapter_; + } + + void on_window_resized(const int width, const int height) override { + width_ = width; + height_ = height; + context_->SetDimensions(Rml::Vector2i(width_, height_)); + update_dp_ratio(); + } + + ~RmlGui() override { + // nothing may keep pointing at this object through the window + window_->set_keyboard_callback(nullptr); + window_->set_gamepad_callback(nullptr); + window_->set_resize_callback(nullptr); + + // releases the GL resources through the backend's render + // interface, whose context is still current on this thread + Rml::Shutdown(); + } + + private: + // Every dp length in menu.rcss is mapped to pixels relative to a + // 1080p design baseline, measured against the monitor the window + // sits on — not the window itself — so the menu keeps the same + // physical size on the display whether the game is fullscreen or + // in a small window (a 4K TV renders it twice as large either + // way). The min of both axes keeps the design fitting on unusual + // ratios. + void update_dp_ratio() const { + const auto [screen_width, screen_height] = window_->screen_size(); + context_->SetDensityIndependentPixelRatio(std::max( + 0.5f, std::min( + static_cast(screen_width) / 1920.0f, + static_cast(screen_height) / 1080.0f))); + } + + // Registered with an explicit family/weight (the static TTFs carry + // per-weight legacy family names that would not match the RCSS + // font-family otherwise). The buffers must outlive Rml::Shutdown(). + void + load_fonts(const std::shared_ptr &asset_reader) { + struct FontSpec { + const char *path; + const char *family; + int weight; + }; + constexpr FontSpec MENU_FONTS[] = { + {.path = "font/Sora-Regular.ttf", .family = "Sora", .weight = 400}, + {.path = "font/Sora-SemiBold.ttf", .family = "Sora", .weight = 600}, + {.path = "font/Sora-Bold.ttf", .family = "Sora", .weight = 700}, + {.path = "font/IBMPlexMono-Regular.ttf", + .family = "IBM Plex Mono", + .weight = 400}, + {.path = "font/IBMPlexMono-Medium.ttf", + .family = "IBM Plex Mono", + .weight = 500}, + {.path = "font/IBMPlexMono-SemiBold.ttf", + .family = "IBM Plex Mono", + .weight = 600}, + }; + + font_buffers_.reserve(std::size(MENU_FONTS)); + for (const auto &[path, family, weight]: MENU_FONTS) { + font_buffers_.push_back(asset_reader->read_text(path)); + const auto &buffer = font_buffers_.back(); + Rml::LoadFontFace( + Rml::Span( + reinterpret_cast(buffer.data()), buffer.size()), + family, Rml::Style::FontStyle::Normal, + static_cast(weight)); + } + } + + void bind_data_model() { + Rml::DataModelConstructor constructor = context_->CreateDataModel("settings"); + if (!constructor) throw std::runtime_error("RmlUi data model creation failed"); + + constructor.RegisterArray>(); + + constructor.Bind("nb_tanks", &settings_.nb_tanks); + constructor.Bind("spawn_side", &settings_.spawn_side); + constructor.Bind("score", &score_); + + graphics_page_.bind(constructor); + controls_page_.bind(constructor); + ai_page_.bind(constructor); + + constructor.BindEventCallback( + "play", [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + if (ai_page_.can_play()) play_clicked_ = true; + }); + constructor.BindEventCallback( + "exit", [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + quit_clicked_ = true; + }); + constructor.BindEventCallback( + "open_params", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + main_document_->Hide(); + params_document_->Show(); + }); + constructor.BindEventCallback( + "back", [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + close_params(); + }); + constructor.BindEventCallback( + "open_graphics", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + params_document_->Hide(); + graphics_document_->Show(); + }); + constructor.BindEventCallback( + "graphics_back", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + close_graphics(); + }); + constructor.BindEventCallback( + "open_controls", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + params_document_->Hide(); + controls_page_.on_open(); + controls_document_->Show(); + }); + constructor.BindEventCallback( + "controls_back", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + close_controls(); + }); + constructor.BindEventCallback( + "open_ai", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + params_document_->Hide(); + ai_document_->Show(); + }); + constructor.BindEventCallback( + "ai_back", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + close_ai(); + }); + + constructor.BindEventCallback( + "pause_continue", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + pending_pause_action_ = PauseAction::Continue; + }); + constructor.BindEventCallback( + "pause_main_menu", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + pending_pause_action_ = PauseAction::MainMenu; + }); + constructor.BindEventCallback( + "pause_exit", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + pending_pause_action_ = PauseAction::ExitGame; + }); + constructor.BindEventCallback( + "game_over_retry", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + pending_pause_action_ = PauseAction::Retry; + }); + + model_handle_ = constructor.GetModelHandle(); + graphics_page_.set_model_handle(model_handle_); + controls_page_.set_model_handle(model_handle_); + ai_page_.set_model_handle(model_handle_); + } + + void close_params() const { + params_document_->Hide(); + main_document_->Show(); + } + + void close_ai() const { + ai_document_->Hide(); + params_document_->Show(); + } + + void close_graphics() const { + graphics_document_->Hide(); + params_document_->Show(); + } + + void close_controls() { + controls_page_.on_close(); + controls_document_->Hide(); + params_document_->Show(); + } + + std::shared_ptr backend_; + std::shared_ptr window_; + + GameSettings settings_; + int width_; + int height_; + + MenuSystemInterface system_interface_; + ReaderBackedFileInterface file_interface_; + + // the pages register their slice of the "settings" data model and + // write straight into settings_ + GraphicsPage graphics_page_; + ControlsPage controls_page_; + AiPage ai_page_; + + // unique_ptr: created after Rml::Initialise(), and member + // destruction keeps it alive until after Rml::Shutdown() as + // RmlUi requires of registered instancers + std::unique_ptr cursor_ring_instancer_; + std::unique_ptr hit_marker_instancer_; + std::unique_ptr reticle_instancer_; + std::unique_ptr damage_arc_instancer_; + std::vector font_buffers_; + + Rml::Context *context_ = nullptr; + Rml::ElementDocument *main_document_ = nullptr; + Rml::ElementDocument *params_document_ = nullptr; + Rml::ElementDocument *controls_document_ = nullptr; + Rml::ElementDocument *graphics_document_ = nullptr; + Rml::ElementDocument *ai_document_ = nullptr; + Rml::ElementDocument *pause_document_ = nullptr; + Rml::ElementDocument *game_over_document_ = nullptr; + Rml::ElementDocument *hud_document_ = nullptr; + // built once hud_document_ is loaded + std::unique_ptr hud_; + Rml::DataModelHandle model_handle_; + + std::shared_ptr input_adapter_; + + // score shown by the pause and game-over popups + int score_ = 0; + bool play_clicked_ = false; + bool quit_clicked_ = false; + PauseAction pending_pause_action_ = PauseAction::None; + }; + + }// namespace + + std::unique_ptr make_gui( + const std::shared_ptr &backend, + const std::shared_ptr &asset_reader, + const GameSettings &initial_settings, const std::vector &gpus, + const int window_width, const int window_height, AgentValidator agent_validator) { + return std::make_unique( + backend, asset_reader, initial_settings, gpus, window_width, window_height, + std::move(agent_validator)); + } + +}// namespace arenai::desktop::gui diff --git a/arenai_desktop/src/gui/rml/hud.cpp b/arenai_desktop/src/gui/rml/hud.cpp new file mode 100644 index 00000000..264f299c --- /dev/null +++ b/arenai_desktop/src/gui/rml/hud.cpp @@ -0,0 +1,83 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./hud.h" + +#include +#include + +namespace arenai::desktop::gui { + + Hud::Hud(Rml::ElementDocument &document) { + reticle_ = document.GetElementById("reticle"); + hit_marker_ = document.GetElementById("hit-marker"); + if (reticle_ == nullptr || hit_marker_ == nullptr) + throw std::runtime_error("hud.rml misses its #reticle / #hit-marker elements"); + + if (Rml::Element *arcs = document.GetElementById("damage-arcs")) + for (int i = 0; i < arcs->GetNumChildren(); i++) + damage_arcs_.push_back(arcs->GetChild(i)); + if (damage_arcs_.empty()) + throw std::runtime_error("hud.rml misses its #damage-arcs elements"); + } + + void Hud::notify_hit(const HitKind kind) const { + const bool kill = kind == HitKind::Kill; + hit_marker_->SetClass("kill", kill); + + // Battlefield-style feedback: the ticks spread outward while + // fading; a kill starts bigger, flares wider and lasts longer + const float duration = kill ? KILL_MARKER_FADE_SECONDS : HIT_MARKER_FADE_SECONDS; + const Rml::Tween tween(Rml::Tween::Quadratic, Rml::Tween::Out); + + const Rml::Property opaque(1.f, Rml::Unit::NUMBER); + hit_marker_->Animate( + "opacity", Rml::Property(0.f, Rml::Unit::NUMBER), duration, tween, 1, false, 0.f, + &opaque); + + const Rml::Property start_scale = Rml::Transform::MakeProperty( + {Rml::Transforms::Scale2D(kill ? KILL_MARKER_START_SCALE : 1.f)}); + hit_marker_->Animate( + "transform", + Rml::Transform::MakeProperty( + {Rml::Transforms::Scale2D(kill ? KILL_MARKER_END_SCALE : HIT_MARKER_END_SCALE)}), + duration, tween, 1, false, 0.f, &start_scale); + } + + void Hud::notify_damage(const float screen_angle) { + // oldest-slot reuse: a burst of impacts shows as many arcs as + // the pool holds, each rotated toward its own shooter + Rml::Element *arc = damage_arcs_[next_damage_arc_]; + next_damage_arc_ = (next_damage_arc_ + 1) % damage_arcs_.size(); + + constexpr float rad_to_deg = 180.f / std::numbers::pi_v; + arc->SetProperty( + Rml::PropertyId::Transform, + Rml::Transform::MakeProperty({Rml::Transforms::Rotate2D(screen_angle * rad_to_deg)})); + + const Rml::Tween tween(Rml::Tween::Quadratic, Rml::Tween::Out); + const Rml::Property opaque(1.f, Rml::Unit::NUMBER); + arc->Animate( + "opacity", Rml::Property(0.f, Rml::Unit::NUMBER), DAMAGE_ARC_FADE_SECONDS, tween, 1, + false, 0.f, &opaque); + } + + void Hud::set_aim_point( + const std::optional normalized, const int width, const int height) const { + if (!normalized) { + reticle_->SetProperty( + Rml::PropertyId::Visibility, Rml::Property(Rml::Style::Visibility::Hidden)); + return; + } + reticle_->SetProperty( + Rml::PropertyId::Visibility, Rml::Property(Rml::Style::Visibility::Visible)); + reticle_->SetProperty( + Rml::PropertyId::Left, + Rml::Property(normalized->x * static_cast(width), Rml::Unit::PX)); + reticle_->SetProperty( + Rml::PropertyId::Top, + Rml::Property(normalized->y * static_cast(height), Rml::Unit::PX)); + } + +}// namespace arenai::desktop::gui diff --git a/arenai_desktop/src/gui/rml/hud.h b/arenai_desktop/src/gui/rml/hud.h new file mode 100644 index 00000000..933fcd5a --- /dev/null +++ b/arenai_desktop/src/gui/rml/hud.h @@ -0,0 +1,46 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_DESKTOP_GUI_RML_HUD_H +#define ARENAI_DESKTOP_GUI_RML_HUD_H + +#include +#include +#include + +#include +#include + +#include "../menu.h" + +namespace arenai::desktop::gui { + + // The in-game overlay elements of hud.rml: the aim reticle, the + // Battlefield-style hit marker and the incoming-damage arc pool. + class Hud { + public: + // grabs the overlay elements from the loaded hud document + explicit Hud(Rml::ElementDocument &document); + + void notify_hit(HitKind kind) const; + void notify_damage(float screen_angle); + void set_aim_point(std::optional normalized, int width, int height) const; + + private: + Rml::Element *reticle_ = nullptr; + Rml::Element *hit_marker_ = nullptr; + std::vector damage_arcs_; + std::size_t next_damage_arc_ = 0; + + static constexpr float DAMAGE_ARC_FADE_SECONDS = 0.8f; + static constexpr float HIT_MARKER_FADE_SECONDS = 0.45f; + static constexpr float HIT_MARKER_END_SCALE = 1.3f; + static constexpr float KILL_MARKER_FADE_SECONDS = 0.6f; + static constexpr float KILL_MARKER_START_SCALE = 1.1f; + static constexpr float KILL_MARKER_END_SCALE = 1.55f; + }; + +}// namespace arenai::desktop::gui + +#endif// ARENAI_DESKTOP_GUI_RML_HUD_H diff --git a/arenai_desktop/src/gui/rml/pages/ai_page.cpp b/arenai_desktop/src/gui/rml/pages/ai_page.cpp new file mode 100644 index 00000000..b8564210 --- /dev/null +++ b/arenai_desktop/src/gui/rml/pages/ai_page.cpp @@ -0,0 +1,161 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./ai_page.h" + +#include +#include +#include + +namespace arenai::desktop::gui { + + AiPage::AiPage(GameSettings &settings, AgentValidator validator) + : settings_(settings), agent_validator_(std::move(validator)), + algorithm_display_(to_string(settings.agent_algorithm)) { + current_dir_ = std::filesystem::exists(settings_.agent_folder) + ? std::filesystem::canonical(settings_.agent_folder) + : std::filesystem::current_path(); + + // the selection persisted from the previous run gets the same + // dry-run check as a freshly picked one + validate_agent(); + } + + void AiPage::bind(Rml::DataModelConstructor &constructor) { + constructor.Bind("algorithm", &algorithm_display_); + constructor.Bind("agent_folder", &agent_folder_display_); + constructor.Bind("agent_config", &agent_config_display_); + constructor.Bind("agent_status", &agent_status_); + constructor.Bind("agent_valid", &agent_valid_); + constructor.Bind("current_dir", ¤t_dir_display_); + constructor.Bind("entries", &entries_); + constructor.Bind("can_play", &can_play_); + + constructor.BindEventCallback( + "select_entry", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (arguments.empty()) return; + const auto index = static_cast(arguments[0].Get()); + if (index >= entries_.size()) return; + + const std::string &entry = entries_[index]; + if (entry == "..") current_dir_ = current_dir_.parent_path(); + else if (const auto path = current_dir_ / entry; + std::filesystem::is_directory(path)) + current_dir_ = path; + else { + // a listed file is a config.json candidate + settings_.agent_config = path; + validate_agent(); + refresh_explorer(); + return; + } + refresh_explorer(); + focus_explorer_pending_ = true; + }); + constructor.BindEventCallback( + "set_algorithm", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (arguments.empty()) return; + const auto algorithm = ai_algorithm_from_string(arguments[0].Get()); + if (!algorithm) return; + settings_.agent_algorithm = *algorithm; + algorithm_display_ = to_string(*algorithm); + model_handle_.DirtyVariable("algorithm"); + // the networks are rebuilt differently: re-run the check + validate_agent(); + refresh_explorer(); + }); + constructor.BindEventCallback( + "select_folder", [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + settings_.agent_folder = current_dir_; + validate_agent(); + refresh_explorer(); + }); + } + + void AiPage::set_model_handle(const Rml::DataModelHandle handle) { model_handle_ = handle; } + + void AiPage::attach(Rml::ElementDocument &document) { + document_ = &document; + document.AddEventListener(Rml::EventId::Keydown, &explorer_nav_listener_); + } + + void AiPage::refresh_explorer() { + entries_.clear(); + if (current_dir_.has_parent_path() && current_dir_ != current_dir_.root_path()) + entries_.emplace_back(".."); + + std::vector config_files; + std::error_code list_error; + for (const auto &entry: std::filesystem::directory_iterator(current_dir_, list_error)) + if (std::error_code type_error; entry.is_directory(type_error)) + entries_.push_back(entry.path().filename().string()); + else if (entry.path().extension() == ".json") + config_files.push_back(entry.path().filename().string()); + if (list_error) + std::cerr << "Cannot list " << current_dir_ << ": " << list_error.message() + << std::endl; + + // keep ".." pinned first, then the directories, then the + // config.json candidates, each block sorted + const auto first_dir = + entries_.begin() + (!entries_.empty() && entries_[0] == ".." ? 1 : 0); + std::sort(first_dir, entries_.end()); + std::sort(config_files.begin(), config_files.end()); + entries_.insert(entries_.end(), config_files.begin(), config_files.end()); + + current_dir_display_ = current_dir_.string(); + agent_folder_display_ = settings_.agent_folder.string(); + agent_config_display_ = settings_.agent_config.empty() + ? "auto (config.json next to the state dicts)" + : settings_.agent_config.string(); + + if (model_handle_) { + model_handle_.DirtyVariable("entries"); + model_handle_.DirtyVariable("current_dir"); + model_handle_.DirtyVariable("agent_folder"); + model_handle_.DirtyVariable("agent_config"); + model_handle_.DirtyVariable("agent_status"); + model_handle_.DirtyVariable("agent_valid"); + model_handle_.DirtyVariable("can_play"); + } + } + + void AiPage::update_after_context() { + // entering a directory rebuilt the entry clones during Update + // (dropping the focused one): put the cursor back on the first entry + // of the fresh listing so the gamepad walk resumes there — invisible + // for the mouse, the :focus highlight only shows under .gamepad-nav + if (std::exchange(focus_explorer_pending_, false)) focus_first_entry(); + } + + // runs the injected dry-run load and turns its outcome into the + // tri-state the AI screen displays (nothing chosen yet / model + // loaded / error message); can_play_ follows the real load + void AiPage::validate_agent() { + if (settings_.agent_folder.empty()) { + agent_valid_ = false; + agent_status_ = ""; + } else { + const auto error = agent_validator_( + {.config = settings_.agent_config, + .folder = settings_.agent_folder, + .algorithm = settings_.agent_algorithm}); + agent_valid_ = !error.has_value(); + agent_status_ = error.value_or("AI model loaded"); + } + can_play_ = agent_valid_; + } + + void AiPage::focus_first_entry() const { + const Rml::Element *list = document_->GetElementById("file-list"); + if (list == nullptr) return; + const auto entries = ExplorerNavListener::visible_file_entries(list); + if (entries.empty()) return; + if (entries.front()->Focus(true)) + entries.front()->ScrollIntoView(Rml::ScrollAlignment::Nearest); + } + +}// namespace arenai::desktop::gui diff --git a/arenai_desktop/src/gui/rml/pages/ai_page.h b/arenai_desktop/src/gui/rml/pages/ai_page.h new file mode 100644 index 00000000..80d2eb0a --- /dev/null +++ b/arenai_desktop/src/gui/rml/pages/ai_page.h @@ -0,0 +1,71 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_DESKTOP_GUI_RML_PAGES_AI_PAGE_H +#define ARENAI_DESKTOP_GUI_RML_PAGES_AI_PAGE_H + +#include +#include + +#include + +#include "../../menu.h" +#include "../input.h" + +namespace arenai::desktop::gui { + + // The AI screen: the algorithm toggle and the file explorer picking the + // trained model's folder, dry-run validated through the injected + // AgentValidator. Registers its slice of the shared "settings" data model + // and writes straight into the GameSettings it was built around. + class AiPage { + public: + AiPage(GameSettings &settings, AgentValidator validator); + + // registers the page's variables and callbacks into the shared model + void bind(Rml::DataModelConstructor &constructor); + void set_model_handle(Rml::DataModelHandle handle); + + // hooks the D-pad bridge across the explorer's scroll container and + // remembers the document for the post-Update focus restore + void attach(Rml::ElementDocument &document); + + void refresh_explorer(); + + // after the context Update rebuilt the entry clones, put the gamepad + // cursor back on the first entry of the fresh listing + void update_after_context(); + + // the menu's Play gate: true once the dry-run load succeeded + bool can_play() const { return can_play_; } + + private: + void validate_agent(); + void focus_first_entry() const; + + GameSettings &settings_; + AgentValidator agent_validator_; + Rml::DataModelHandle model_handle_; + + Rml::ElementDocument *document_ = nullptr; + // removed from the document when Rml::Shutdown() destroys it, before + // the members are torn down + ExplorerNavListener explorer_nav_listener_; + bool focus_explorer_pending_ = false; + + std::filesystem::path current_dir_; + Rml::String algorithm_display_; + Rml::String current_dir_display_; + Rml::String agent_folder_display_; + Rml::String agent_config_display_; + // empty while no folder is chosen; otherwise success or error text + Rml::String agent_status_; + std::vector entries_; + bool agent_valid_ = false; + bool can_play_ = false; + }; + +}// namespace arenai::desktop::gui + +#endif// ARENAI_DESKTOP_GUI_RML_PAGES_AI_PAGE_H diff --git a/arenai_desktop/src/gui/rml/pages/controls_page.cpp b/arenai_desktop/src/gui/rml/pages/controls_page.cpp new file mode 100644 index 00000000..61af1cdb --- /dev/null +++ b/arenai_desktop/src/gui/rml/pages/controls_page.cpp @@ -0,0 +1,379 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./controls_page.h" + +#include +#include +#include +#include + +#include + +namespace arenai::desktop::gui { + + namespace { + + // slot order of the two pages; the gamepad page puts its button slot + // (fire) first, then the five axis slots + constexpr const char *KB_SLOT_LABELS[] = { + "FORWARD", "BACKWARD", "TURN LEFT", "TURN RIGHT", "FIRE"}; + constexpr const char *GP_SLOT_LABELS[] = {"FIRE", "STEER", "AIM X", + "AIM Y", "ACCELERATE", "REVERSE"}; + constexpr int NB_KB_SLOTS = 5; + constexpr int NB_GP_SLOTS = 6; + // accelerate / reverse read a single direction of their axis + constexpr bool gp_slot_is_one_way(const int slot) { return slot >= 4; } + + // an axis must come back to rest before it can be captured: without + // this the stick still deflected from navigating the menu (or the A + // press bound to a trigger) would bind itself instantly + constexpr double CAPTURE_ENGAGE = 0.6, CAPTURE_REST = 0.3; + + // a capture nobody feeds cancels itself: Escape is the only cancel + // input (every pad button stays bindable), so a pad-only player + // needs the timeout + constexpr auto CAPTURE_TIMEOUT = std::chrono::seconds(15); + + constexpr const char *GAMEPAD_AXIS_LABELS[] = {"L-STICK X", "L-STICK Y", "R-STICK X", + "R-STICK Y", "LT", "RT"}; + + }// namespace + + ControlsPage::ControlsPage(GameSettings &settings, std::shared_ptr window) + : settings_(settings), window_(std::move(window)), + controller_display_( + settings.controller_kind == ControllerKind::Gamepad ? "gamepad" : "keyboard") {} + + void ControlsPage::bind(Rml::DataModelConstructor &constructor) { + if (auto row_handle = constructor.RegisterStruct()) { + row_handle.RegisterMember("label", &BindingRow::label); + row_handle.RegisterMember("binding", &BindingRow::binding); + row_handle.RegisterMember("listening", &BindingRow::listening); + row_handle.RegisterMember("bound", &BindingRow::bound); + } + constructor.RegisterArray>(); + + constructor.Bind("kb_rows", &kb_rows_); + constructor.Bind("gp_rows", &gp_rows_); + constructor.Bind("gamepads", &gamepad_names_); + constructor.Bind("selected_gamepad", &selected_gamepad_); + constructor.Bind("bind_status", &bind_status_); + constructor.Bind("bind_warning", &bind_warning_); + constructor.Bind("controller", &controller_display_); + + constructor.BindEventCallback( + "set_controller", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (arguments.empty()) return; + controller_display_ = arguments[0].Get(); + settings_.controller_kind = controller_display_ == "gamepad" + ? ControllerKind::Gamepad + : ControllerKind::Keyboard; + model_handle_.DirtyVariable("controller"); + // the toggle lives on the controls page: switching the + // kind swaps the visible bindings, so a running capture + // and the status text follow along + end_capture(); + set_default_bind_status(); + rebuild_binding_rows(); + }); + constructor.BindEventCallback( + "capture_kb", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (!arguments.empty()) begin_capture(true, arguments[0].Get()); + }); + constructor.BindEventCallback( + "capture_gp", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (!arguments.empty()) begin_capture(false, arguments[0].Get()); + }); + constructor.BindEventCallback( + "select_pad", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (arguments.empty()) return; + const auto index = static_cast(arguments[0].Get()); + if (index >= gamepads_.size()) return; + + settings_.bindings.gamepad.device_guid = gamepads_[index].guid; + settings_.bindings.gamepad.device_name = gamepads_[index].name; + window_->select_gamepad(gamepads_[index].id); + selected_gamepad_ = static_cast(index); + model_handle_.DirtyVariable("selected_gamepad"); + }); + constructor.BindEventCallback( + "reset_bindings", [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { + end_capture(); + if (settings_.controller_kind == ControllerKind::Keyboard) + settings_.bindings.keyboard = {}; + else { + // the device choice is not a binding: keep it + auto gamepad = GamepadBindings{}; + gamepad.device_guid = std::move(settings_.bindings.gamepad.device_guid); + gamepad.device_name = std::move(settings_.bindings.gamepad.device_name); + settings_.bindings.gamepad = std::move(gamepad); + } + set_default_bind_status(); + rebuild_binding_rows(); + }); + } + + void ControlsPage::set_model_handle(const Rml::DataModelHandle handle) { + model_handle_ = handle; + } + + void ControlsPage::set_input_adapter(std::shared_ptr adapter) { + input_adapter_ = std::move(adapter); + } + + void ControlsPage::on_open() { + refresh_gamepad_list(); + set_default_bind_status(); + rebuild_binding_rows(); + } + + void ControlsPage::on_close() { + end_capture(); + rebuild_binding_rows(); + } + + void ControlsPage::update(const bool page_visible) { + // pads can be (un)plugged while the device list is on screen; the + // refresh is a no-op while nothing changed + if (page_visible && settings_.controller_kind == ControllerKind::Gamepad) + refresh_gamepad_list(); + + // a forgotten capture ends on its own (a pad-only player has no + // Escape at hand) + if (capture_slot_ >= 0 && std::chrono::steady_clock::now() >= capture_deadline_) { + end_capture(); + rebuild_binding_rows(); + } + } + + std::array *, 5> ControlsPage::kb_slots() { + auto &keyboard = settings_.bindings.keyboard; + return { + &keyboard.forward, &keyboard.backward, &keyboard.turn_left, &keyboard.turn_right, + &keyboard.fire}; + } + + std::array *, 5> ControlsPage::gp_axis_slots() { + auto &gamepad = settings_.bindings.gamepad; + return { + &gamepad.steer, &gamepad.aim_x, &gamepad.aim_y, &gamepad.accelerate, &gamepad.reverse}; + } + + Rml::String + ControlsPage::keyboard_slot_label(const std::optional &slot) const { + if (!slot) return "UNBOUND"; + if (const auto *button = std::get_if(&*slot)) switch (*button) { + case controller::MouseButton::Left: return "MOUSE L"; + case controller::MouseButton::Right: return "MOUSE R"; + case controller::MouseButton::Middle: return "MOUSE M"; + } + // layout-aware label (Key::Q reads "A" on AZERTY) + const auto label = window_->key_label(std::get(*slot)); + return label.empty() ? "?" : Rml::String(label); + } + + Rml::String ControlsPage::axis_slot_label( + const std::optional &slot, const bool one_way) { + if (!slot) return "UNBOUND"; + Rml::String label = GAMEPAD_AXIS_LABELS[static_cast(slot->axis)]; + // a one-way action bound to a stick reads a single direction + const bool on_stick = + slot->axis != GamepadAxis::LeftTrigger && slot->axis != GamepadAxis::RightTrigger; + if (one_way && on_stick) label += slot->sign > 0.f ? "+" : "-"; + return label; + } + + void ControlsPage::rebuild_binding_rows() { + kb_rows_.resize(NB_KB_SLOTS); + const auto keyboard_slots = kb_slots(); + for (int i = 0; i < NB_KB_SLOTS; i++) { + const bool listening = capture_keyboard_page_ && capture_slot_ == i; + kb_rows_[i] = { + .label = KB_SLOT_LABELS[i], + .binding = listening ? "PRESS A KEY..." : keyboard_slot_label(*keyboard_slots[i]), + .listening = listening, + .bound = keyboard_slots[i]->has_value()}; + } + + gp_rows_.resize(NB_GP_SLOTS); + const auto axis_slots = gp_axis_slots(); + for (int i = 0; i < NB_GP_SLOTS; i++) { + const bool listening = !capture_keyboard_page_ && capture_slot_ == i; + const bool fire_slot = i == 0; + Rml::String binding; + if (listening) binding = fire_slot ? "PRESS A BUTTON..." : "MOVE AN AXIS..."; + else if (fire_slot) + binding = settings_.bindings.gamepad.fire + ? controller::to_string(*settings_.bindings.gamepad.fire) + : "UNBOUND"; + else binding = axis_slot_label(*axis_slots[i - 1], gp_slot_is_one_way(i)); + gp_rows_[i] = { + .label = GP_SLOT_LABELS[i], + .binding = std::move(binding), + .listening = listening, + .bound = fire_slot ? settings_.bindings.gamepad.fire.has_value() + : axis_slots[i - 1]->has_value()}; + } + + if (model_handle_) { + model_handle_.DirtyVariable("kb_rows"); + model_handle_.DirtyVariable("gp_rows"); + model_handle_.DirtyVariable("bind_status"); + model_handle_.DirtyVariable("bind_warning"); + } + } + + void ControlsPage::set_default_bind_status() { + bind_warning_ = false; + bind_status_ = settings_.controller_kind == ControllerKind::Keyboard + ? "Click a slot, then press the new key or mouse button. " + "Escape cancels." + : "Click a slot, then press a button or move an axis. " + "Escape or 15s of inactivity cancels — Start stays the " + "pause toggle."; + } + + void ControlsPage::begin_capture(const bool keyboard_page, const int slot) { + const int nb_slots = keyboard_page ? NB_KB_SLOTS : NB_GP_SLOTS; + if (slot < 0 || slot >= nb_slots) return; + + capture_keyboard_page_ = keyboard_page; + capture_slot_ = slot; + capture_deadline_ = std::chrono::steady_clock::now() + CAPTURE_TIMEOUT; + // every axis must return to rest once before it can bind (the + // stick may still be deflected from navigating the menu) + axis_armed_.fill(false); + set_default_bind_status(); + + input_adapter_->set_capture_sink( + [this](const RawMenuInput &input) { on_capture_input(input); }); + rebuild_binding_rows(); + } + + void ControlsPage::end_capture() { + capture_slot_ = -1; + if (input_adapter_) input_adapter_->set_capture_sink(nullptr); + } + + void ControlsPage::on_capture_input(const RawMenuInput &input) { + // Escape is the only cancel input (every pad button stays + // bindable); a pad-only player relies on the capture timeout + if (const auto *key = std::get_if(&input); + key != nullptr && *key == controller::Key::Escape) { + end_capture(); + rebuild_binding_rows(); + return; + } + + if (capture_keyboard_page_) { + if (const auto *key = std::get_if(&input)) + assign_keyboard(KeyboardBinding(*key)); + else if (const auto *button = std::get_if(&input)) + assign_keyboard(KeyboardBinding(*button)); + // pad input has no meaning on the keyboard page + return; + } + + if (capture_slot_ == 0) { + // Start stays the in-game pause toggle, never a binding + if (const auto *button = std::get_if(&input); + button != nullptr && *button != controller::GamepadButton::Start) + assign_gamepad_fire(*button); + return; + } + + if (const auto *motion = std::get_if>(&input)) { + const auto &[axis, value] = *motion; + auto &armed = axis_armed_[static_cast(axis)]; + if (std::abs(value) < CAPTURE_REST) armed = true; + else if (armed && std::abs(value) > CAPTURE_ENGAGE) assign_gamepad_axis(axis, value); + } + } + + void ControlsPage::unbind_conflict(const Rml::String &new_label, const Rml::String &old_label) { + bind_warning_ = true; + bind_status_ = + new_label + " was bound to " + old_label + " — " + old_label + " is now unbound."; + } + + void ControlsPage::assign_keyboard(const KeyboardBinding &binding) { + const auto slots = kb_slots(); + *slots[capture_slot_] = binding; + + for (int i = 0; i < NB_KB_SLOTS; i++) + if (i != capture_slot_ && *slots[i] == binding) { + *slots[i] = std::nullopt; + unbind_conflict(keyboard_slot_label(binding), KB_SLOT_LABELS[i]); + } + + end_capture(); + rebuild_binding_rows(); + } + + void ControlsPage::assign_gamepad_fire(const controller::GamepadButton button) { + // single button slot: no conflict possible + settings_.bindings.gamepad.fire = button; + end_capture(); + rebuild_binding_rows(); + } + + void ControlsPage::assign_gamepad_axis(const GamepadAxis axis, const double value) { + const bool one_way = gp_slot_is_one_way(capture_slot_); + const auto slots = gp_axis_slots(); + const GamepadAxisBinding binding{.axis = axis, .sign = one_way && value < 0. ? -1.f : 1.f}; + *slots[capture_slot_ - 1] = binding; + + // two slots clash when they read the same range of an axis: a + // two-way slot owns the whole axis, one-way slots only their + // captured side + for (int slot = 1; slot < NB_GP_SLOTS; slot++) { + if (slot == capture_slot_) continue; + auto &other = *slots[slot - 1]; + if (!other || other->axis != axis) continue; + if (one_way && gp_slot_is_one_way(slot) && other->sign != binding.sign) continue; + other = std::nullopt; + unbind_conflict(axis_slot_label(binding, one_way), GP_SLOT_LABELS[slot]); + } + + end_capture(); + rebuild_binding_rows(); + } + + void ControlsPage::refresh_gamepad_list() { + auto gamepads = window_->list_gamepads(); + const bool changed = gamepads.size() != gamepads_.size() + || !std::equal( + gamepads.begin(), gamepads.end(), gamepads_.begin(), + [](const view::GamepadInfo &a, const view::GamepadInfo &b) { + return a.id == b.id && a.guid == b.guid && a.name == b.name; + }); + if (!changed) return; + + gamepads_ = std::move(gamepads); + gamepad_names_.clear(); + for (const auto &pad: gamepads_) gamepad_names_.emplace_back(pad.name); + + // the preferred pad when connected, else the first one (which + // is what the window falls back to) + selected_gamepad_ = gamepads_.empty() ? -1 : 0; + for (size_t i = 0; i < gamepads_.size(); i++) + if (!settings_.bindings.gamepad.device_guid.empty() + && gamepads_[i].guid == settings_.bindings.gamepad.device_guid) { + selected_gamepad_ = static_cast(i); + break; + } + window_->select_gamepad(selected_gamepad_ >= 0 ? gamepads_[selected_gamepad_].id : -1); + + if (model_handle_) { + model_handle_.DirtyVariable("gamepads"); + model_handle_.DirtyVariable("selected_gamepad"); + } + } + +}// namespace arenai::desktop::gui diff --git a/arenai_desktop/src/gui/rml/pages/controls_page.h b/arenai_desktop/src/gui/rml/pages/controls_page.h new file mode 100644 index 00000000..3ae9b470 --- /dev/null +++ b/arenai_desktop/src/gui/rml/pages/controls_page.h @@ -0,0 +1,101 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_DESKTOP_GUI_RML_PAGES_CONTROLS_PAGE_H +#define ARENAI_DESKTOP_GUI_RML_PAGES_CONTROLS_PAGE_H + +#include +#include +#include +#include +#include + +#include + +#include + +#include "../../../controller/bindings.h" +#include "../../menu.h" +#include "../input.h" + +namespace arenai::desktop::gui { + + // one row of the controls page: an action and its current binding + struct BindingRow { + Rml::String label; + Rml::String binding; + bool listening = false; + bool bound = true; + }; + + // The Controls screen: the input-kind toggle, the gamepad device list and + // the keyboard / gamepad binding rows with their capture state machine. + // Registers its slice of the shared "settings" data model and writes + // straight into the GameSettings it was built around. + class ControlsPage { + public: + ControlsPage(GameSettings &settings, std::shared_ptr window); + + // registers the page's variables and callbacks into the shared model + void bind(Rml::DataModelConstructor &constructor); + void set_model_handle(Rml::DataModelHandle handle); + // captures route the raw input through the menu input adapter, which + // only exists once the documents are loaded + void set_input_adapter(std::shared_ptr adapter); + + void on_open(); + void on_close(); + + // per-tick upkeep of the main-menu loop: gamepad hot-(un)plug while + // the page is on screen, and the capture timeout + void update(bool page_visible); + + private: + std::array *, 5> kb_slots(); + // gamepad slots 1..5 (0 is the fire button) + std::array *, 5> gp_axis_slots(); + + Rml::String keyboard_slot_label(const std::optional &slot) const; + static Rml::String + axis_slot_label(const std::optional &slot, bool one_way); + + void rebuild_binding_rows(); + void set_default_bind_status(); + + void begin_capture(bool keyboard_page, int slot); + void end_capture(); + void on_capture_input(const RawMenuInput &input); + + void unbind_conflict(const Rml::String &new_label, const Rml::String &old_label); + void assign_keyboard(const KeyboardBinding &binding); + void assign_gamepad_fire(controller::GamepadButton button); + void assign_gamepad_axis(GamepadAxis axis, double value); + + void refresh_gamepad_list(); + + GameSettings &settings_; + std::shared_ptr window_; + std::shared_ptr input_adapter_; + Rml::DataModelHandle model_handle_; + + Rml::String controller_display_; + std::vector kb_rows_; + std::vector gp_rows_; + std::vector gamepads_; + std::vector gamepad_names_; + // index into gamepads_ of the pad feeding the game, -1 when none + // is connected + int selected_gamepad_ = -1; + Rml::String bind_status_; + bool bind_warning_ = false; + // slot being captured (-1 = idle) and which page it belongs to + int capture_slot_ = -1; + bool capture_keyboard_page_ = false; + std::chrono::steady_clock::time_point capture_deadline_; + std::array axis_armed_{}; + }; + +}// namespace arenai::desktop::gui + +#endif// ARENAI_DESKTOP_GUI_RML_PAGES_CONTROLS_PAGE_H diff --git a/arenai_desktop/src/gui/rml/pages/graphics_page.cpp b/arenai_desktop/src/gui/rml/pages/graphics_page.cpp new file mode 100644 index 00000000..8d90cfe7 --- /dev/null +++ b/arenai_desktop/src/gui/rml/pages/graphics_page.cpp @@ -0,0 +1,106 @@ +// +// Created by samuel on 06/09/2026. +// + +#include "./graphics_page.h" + +#include +#include + +namespace arenai::desktop::gui { + + GraphicsPage::GraphicsPage( + GameSettings &settings, std::shared_ptr window, + const std::vector &gpus) + : settings_(settings), window_(std::move(window)), + display_display_(settings.fullscreen ? "fullscreen" : "windowed"), + shadow_display_(to_string(settings.shadow_quality)) { + gpu_names_.emplace_back("Auto"); + for (const auto &gpu: gpus) gpu_names_.emplace_back(gpu); + + const auto gpu_index = [this](const std::string &name) -> int { + for (size_t i = 1; i < gpu_names_.size(); i++) + if (!name.empty() && gpu_names_[i] == name) return static_cast(i); + return 0; + }; + selected_window_gpu_ = gpu_index(settings_.window_gpu); + selected_vision_gpu_ = gpu_index(settings_.vision_gpu); + // a saved GPU that disappeared falls back to Auto, re-saved as such + if (selected_window_gpu_ == 0) settings_.window_gpu.clear(); + if (selected_vision_gpu_ == 0) settings_.vision_gpu.clear(); + + window_gpu_env_override_ = std::getenv("ARENAI_VK_DEVICE_WINDOW") != nullptr; + vision_gpu_env_override_ = std::getenv("ARENAI_VK_DEVICE") != nullptr; + } + + void GraphicsPage::bind(Rml::DataModelConstructor &constructor) { + constructor.Bind("display", &display_display_); + constructor.Bind("shadow_quality", &shadow_display_); + constructor.Bind("msaa", &settings_.msaa_samples); + constructor.Bind("gpus", &gpu_names_); + constructor.Bind("selected_window_gpu", &selected_window_gpu_); + constructor.Bind("selected_vision_gpu", &selected_vision_gpu_); + constructor.Bind("window_gpu_env", &window_gpu_env_override_); + constructor.Bind("vision_gpu_env", &vision_gpu_env_override_); + + constructor.BindEventCallback( + "set_display", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (arguments.empty()) return; + display_display_ = arguments[0].Get(); + settings_.fullscreen = display_display_ == "fullscreen"; + // applied immediately; the window reports its new size + // through the resize callback (dp-ratio included) + window_->set_fullscreen(settings_.fullscreen); + model_handle_.DirtyVariable("display"); + }); + constructor.BindEventCallback( + "set_shadow_quality", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (arguments.empty()) return; + const auto quality = shadow_quality_from_string(arguments[0].Get()); + if (!quality) return; + settings_.shadow_quality = *quality; + shadow_display_ = to_string(*quality); + model_handle_.DirtyVariable("shadow_quality"); + }); + constructor.BindEventCallback( + "set_msaa", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (arguments.empty()) return; + const int samples = arguments[0].Get(); + if (samples != 1 && samples != 2 && samples != 4 && samples != 8) return; + settings_.msaa_samples = samples; + model_handle_.DirtyVariable("msaa"); + }); + constructor.BindEventCallback( + "set_window_gpu", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (arguments.empty()) return; + select_gpu( + arguments[0].Get(), selected_window_gpu_, settings_.window_gpu, + "selected_window_gpu"); + }); + constructor.BindEventCallback( + "set_vision_gpu", + [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { + if (arguments.empty()) return; + select_gpu( + arguments[0].Get(), selected_vision_gpu_, settings_.vision_gpu, + "selected_vision_gpu"); + }); + } + + void GraphicsPage::set_model_handle(const Rml::DataModelHandle handle) { + model_handle_ = handle; + } + + void GraphicsPage::select_gpu( + const int index, int &selected, std::string &setting, const char *variable) { + if (index < 0 || index >= static_cast(gpu_names_.size())) return; + selected = index; + setting = index == 0 ? std::string() : std::string(gpu_names_[index]); + model_handle_.DirtyVariable(variable); + } + +}// namespace arenai::desktop::gui diff --git a/arenai_desktop/src/gui/rml/pages/graphics_page.h b/arenai_desktop/src/gui/rml/pages/graphics_page.h new file mode 100644 index 00000000..6a662ad9 --- /dev/null +++ b/arenai_desktop/src/gui/rml/pages/graphics_page.h @@ -0,0 +1,53 @@ +// +// Created by samuel on 06/09/2026. +// + +#ifndef ARENAI_DESKTOP_GUI_RML_PAGES_GRAPHICS_PAGE_H +#define ARENAI_DESKTOP_GUI_RML_PAGES_GRAPHICS_PAGE_H + +#include +#include +#include + +#include + +#include + +#include "../../menu.h" + +namespace arenai::desktop::gui { + + // The Graphics screen: display mode, shadow quality, MSAA and the two GPU + // pickers. Registers its slice of the shared "settings" data model and + // writes straight into the GameSettings it was built around. + class GraphicsPage { + public: + GraphicsPage( + GameSettings &settings, std::shared_ptr window, + const std::vector &gpus); + + // registers the page's variables and callbacks into the shared model + void bind(Rml::DataModelConstructor &constructor); + void set_model_handle(Rml::DataModelHandle handle); + + private: + // "Auto" rides index 0 of gpu_names_, the actual devices follow + void select_gpu(int index, int &selected, std::string &setting, const char *variable); + + GameSettings &settings_; + std::shared_ptr window_; + + Rml::String display_display_; + Rml::String shadow_display_; + std::vector gpu_names_; + int selected_window_gpu_ = 0; + int selected_vision_gpu_ = 0; + bool window_gpu_env_override_ = false; + bool vision_gpu_env_override_ = false; + + Rml::DataModelHandle model_handle_; + }; + +}// namespace arenai::desktop::gui + +#endif// ARENAI_DESKTOP_GUI_RML_PAGES_GRAPHICS_PAGE_H diff --git a/arenai_desktop/src/gui/rml_menu.cpp b/arenai_desktop/src/gui/rml_menu.cpp deleted file mode 100644 index 397e37df..00000000 --- a/arenai_desktop/src/gui/rml_menu.cpp +++ /dev/null @@ -1,1066 +0,0 @@ -// -// Created by samuel on 17/07/2026. -// - -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include - -#include -#include -#include - -#include "./menu.h" -#include "./rml/adapters.h" -#include "./rml/cursor_ring.h" -#include "./rml/damage_arc.h" -#include "./rml/hit_marker.h" -#include "./rml/input.h" -#include "./rml/reticle.h" - -namespace arenai::desktop::gui { - - namespace { - - // one row of the controls page: an action and its current binding - struct BindingRow { - Rml::String label; - Rml::String binding; - bool listening = false; - bool bound = true; - }; - - // slot order of the two pages; the gamepad page puts its button slot - // (fire) first, then the five axis slots - constexpr const char *KB_SLOT_LABELS[] = { - "FORWARD", "BACKWARD", "TURN LEFT", "TURN RIGHT", "FIRE"}; - constexpr const char *GP_SLOT_LABELS[] = {"FIRE", "STEER", "AIM X", - "AIM Y", "ACCELERATE", "REVERSE"}; - constexpr int NB_KB_SLOTS = 5; - constexpr int NB_GP_SLOTS = 6; - // accelerate / reverse read a single direction of their axis - constexpr bool gp_slot_is_one_way(const int slot) { return slot >= 4; } - - // an axis must come back to rest before it can be captured: without - // this the stick still deflected from navigating the menu (or the A - // press bound to a trigger) would bind itself instantly - constexpr double CAPTURE_ENGAGE = 0.6, CAPTURE_REST = 0.3; - - // a capture nobody feeds cancels itself: Escape is the only cancel - // input (every pad button stays bindable), so a pad-only player - // needs the timeout - constexpr auto CAPTURE_TIMEOUT = std::chrono::seconds(15); - - constexpr const char *GAMEPAD_AXIS_LABELS[] = {"L-STICK X", "L-STICK Y", "R-STICK X", - "R-STICK Y", "LT", "RT"}; - - class RmlGui final : public AbstractGui { - public: - RmlGui( - const std::shared_ptr &backend, - const std::shared_ptr &asset_reader, - const GameSettings &initial_settings, const std::vector &gpus, - const int window_width, const int window_height, SacFolderValidator sac_validator) - : backend_(backend), window_(backend->get_window()), settings_(initial_settings), - width_(window_width), height_(window_height), file_interface_(asset_reader), - sac_validator_(std::move(sac_validator)), - controller_display_( - initial_settings.controller_kind == ControllerKind::Gamepad ? "gamepad" - : "keyboard"), - display_display_(initial_settings.fullscreen ? "fullscreen" : "windowed"), - shadow_display_(to_string(initial_settings.shadow_quality)) { - gpu_names_.emplace_back("Auto"); - for (const auto &gpu: gpus) gpu_names_.emplace_back(gpu); - - const auto gpu_index = [this](const std::string &name) -> int { - for (size_t i = 1; i < gpu_names_.size(); i++) - if (!name.empty() && gpu_names_[i] == name) return static_cast(i); - return 0; - }; - selected_window_gpu_ = gpu_index(settings_.window_gpu); - selected_vision_gpu_ = gpu_index(settings_.vision_gpu); - // a saved GPU that disappeared falls back to Auto, re-saved as such - if (selected_window_gpu_ == 0) settings_.window_gpu.clear(); - if (selected_vision_gpu_ == 0) settings_.vision_gpu.clear(); - - window_gpu_env_override_ = std::getenv("ARENAI_VK_DEVICE_WINDOW") != nullptr; - vision_gpu_env_override_ = std::getenv("ARENAI_VK_DEVICE") != nullptr; - - Rml::SetSystemInterface(&system_interface_); - Rml::SetFileInterface(&file_interface_); - Rml::SetRenderInterface(&backend_->ui_render_interface()); - Rml::Initialise(); - - // menu.rcss draws the slider knob's detached cursor ring with - // this gui-local decorator. Built only now: its property - // registration needs the style-sheet specification that - // Rml::Initialise() just created, so it cannot be a plain - // member (members are constructed before this body runs). - cursor_ring_instancer_ = std::make_unique(); - Rml::Factory::RegisterDecoratorInstancer( - "cursor-ring", cursor_ring_instancer_.get()); - hit_marker_instancer_ = std::make_unique(); - Rml::Factory::RegisterDecoratorInstancer("hit-marker", hit_marker_instancer_.get()); - reticle_instancer_ = std::make_unique(); - Rml::Factory::RegisterDecoratorInstancer("reticle", reticle_instancer_.get()); - damage_arc_instancer_ = std::make_unique(); - Rml::Factory::RegisterDecoratorInstancer("damage-arc", damage_arc_instancer_.get()); - - load_fonts(asset_reader); - - context_ = Rml::CreateContext("menu", Rml::Vector2i(width_, height_)); - if (!context_) throw std::runtime_error("RmlUi context creation failed"); - update_dp_ratio(); - - current_dir_ = std::filesystem::exists(settings_.sac_folder) - ? std::filesystem::canonical(settings_.sac_folder) - : std::filesystem::current_path(); - - // the folder persisted from the previous run gets the same - // dry-run check as a freshly picked one - validate_sac_folder(); - - bind_data_model(); - refresh_explorer(); - - main_document_ = context_->LoadDocument("menu/main_menu.rml"); - params_document_ = context_->LoadDocument("menu/parameters.rml"); - controls_document_ = context_->LoadDocument("menu/controls.rml"); - graphics_document_ = context_->LoadDocument("menu/graphics.rml"); - pause_document_ = context_->LoadDocument("menu/pause.rml"); - game_over_document_ = context_->LoadDocument("menu/game_over.rml"); - hud_document_ = context_->LoadDocument("menu/hud.rml"); - if (!main_document_ || !params_document_ || !controls_document_ - || !graphics_document_ || !pause_document_ || !game_over_document_ - || !hud_document_) - throw std::runtime_error("RmlUi menu documents failed to load"); - - // D-pad bridge across the file explorer's scroll container - reticle_ = hud_document_->GetElementById("reticle"); - hit_marker_ = hud_document_->GetElementById("hit-marker"); - if (reticle_ == nullptr || hit_marker_ == nullptr) - throw std::runtime_error("hud.rml misses its #reticle / #hit-marker elements"); - - if (Rml::Element *arcs = hud_document_->GetElementById("damage-arcs")) - for (int i = 0; i < arcs->GetNumChildren(); i++) - damage_arcs_.push_back(arcs->GetChild(i)); - if (damage_arcs_.empty()) - throw std::runtime_error("hud.rml misses its #damage-arcs elements"); - - hud_document_->Show(Rml::ModalFlag::None, Rml::FocusFlag::None); - - params_document_->AddEventListener(Rml::EventId::Keydown, &explorer_nav_listener_); - - input_adapter_ = std::make_shared( - context_, - [this] { - // Escape / gamepad B back out of the controls and - // parameters screens; while paused B resumes the game - // (the application intercepts Escape itself before - // this adapter); the game-over popup cannot be backed - // out of - if (game_over_document_->IsVisible()) return; - if (pause_document_->IsVisible()) - pending_pause_action_ = PauseAction::Continue; - else if (controls_document_->IsVisible()) close_controls(); - else if (graphics_document_->IsVisible()) close_graphics(); - else if (params_document_->IsVisible()) close_params(); - }, - [this](const bool gamepad) { - // menu.rcss shows the :focus highlight only under - // .gamepad-nav, so the mouse hover and the gamepad - // cursor are never visible together - for (auto *document: - {main_document_, params_document_, controls_document_, - graphics_document_, pause_document_, game_over_document_}) - document->SetClass("gamepad-nav", gamepad); - }); - - // route the pad input to the pad persisted from the previous - // session, when it is connected - refresh_gamepad_list(); - rebuild_binding_rows(); - } - - MenuOutcome run_main_menu() override { - play_clicked_ = false; - quit_clicked_ = false; - - window_->set_keyboard_callback(input_adapter_); - window_->set_gamepad_callback(input_adapter_); - window_->set_cursor_mode(controller::CursorMode::Normal); - main_document_->Show(); - - while (!window_->should_close() && !play_clicked_ && !quit_clicked_) { - window_->poll_events(); - - // pads can be (un)plugged while the device list is on - // screen; the refresh is a no-op while nothing changed - if (controls_document_->IsVisible() - && settings_.controller_kind == ControllerKind::Gamepad) - refresh_gamepad_list(); - - // a forgotten capture ends on its own (a pad-only player - // has no Escape at hand) - if (capture_slot_ >= 0 - && std::chrono::steady_clock::now() >= capture_deadline_) { - end_capture(); - rebuild_binding_rows(); - } - - context_->Update(); - - // entering a directory rebuilt the entry clones during - // Update (dropping the focused one): put the cursor back - // on the first entry of the fresh listing so the gamepad - // walk resumes there — invisible for the mouse, the - // :focus highlight only shows under .gamepad-nav - if (std::exchange(focus_explorer_pending_, false)) focus_first_entry(); - - backend_->begin_ui_frame(width_, height_); - context_->Render(); - backend_->end_ui_frame(); - backend_->present(); - } - - main_document_->Hide(); - params_document_->Hide(); - graphics_document_->Hide(); - end_capture(); - controls_document_->Hide(); - window_->set_keyboard_callback(nullptr); - window_->set_gamepad_callback(nullptr); - - return play_clicked_ ? MenuOutcome::Play : MenuOutcome::Quit; - } - - GameSettings settings() const override { return settings_; } - - void open_pause(const int score) override { - pending_pause_action_ = PauseAction::None; - score_ = score; - model_handle_.DirtyVariable("score"); - pause_document_->Show(); - } - - void close_pause() override { pause_document_->Hide(); } - - void open_game_over(const int score) override { - pending_pause_action_ = PauseAction::None; - score_ = score; - model_handle_.DirtyVariable("score"); - game_over_document_->Show(); - } - - void close_game_over() override { game_over_document_->Hide(); } - - void render_pause_overlay() override { - context_->Update(); - - backend_->begin_ui_overlay(width_, height_); - context_->Render(); - backend_->end_ui_frame(); - } - - PauseAction poll_pause_action() override { - return std::exchange(pending_pause_action_, PauseAction::None); - } - - void notify_hit(const HitKind kind) override { - const bool kill = kind == HitKind::Kill; - hit_marker_->SetClass("kill", kill); - - // Battlefield-style feedback: the ticks spread outward while - // fading; a kill starts bigger, flares wider and lasts longer - const float duration = kill ? KILL_MARKER_FADE_SECONDS : HIT_MARKER_FADE_SECONDS; - const Rml::Tween tween(Rml::Tween::Quadratic, Rml::Tween::Out); - - const Rml::Property opaque(1.f, Rml::Unit::NUMBER); - hit_marker_->Animate( - "opacity", Rml::Property(0.f, Rml::Unit::NUMBER), duration, tween, 1, false, - 0.f, &opaque); - - const Rml::Property start_scale = Rml::Transform::MakeProperty( - {Rml::Transforms::Scale2D(kill ? KILL_MARKER_START_SCALE : 1.f)}); - hit_marker_->Animate( - "transform", - Rml::Transform::MakeProperty({Rml::Transforms::Scale2D( - kill ? KILL_MARKER_END_SCALE : HIT_MARKER_END_SCALE)}), - duration, tween, 1, false, 0.f, &start_scale); - } - - void notify_damage(const float screen_angle) override { - // oldest-slot reuse: a burst of impacts shows as many arcs as - // the pool holds, each rotated toward its own shooter - Rml::Element *arc = damage_arcs_[next_damage_arc_]; - next_damage_arc_ = (next_damage_arc_ + 1) % damage_arcs_.size(); - - constexpr float rad_to_deg = 180.f / std::numbers::pi_v; - arc->SetProperty( - Rml::PropertyId::Transform, - Rml::Transform::MakeProperty( - {Rml::Transforms::Rotate2D(screen_angle * rad_to_deg)})); - - const Rml::Tween tween(Rml::Tween::Quadratic, Rml::Tween::Out); - const Rml::Property opaque(1.f, Rml::Unit::NUMBER); - arc->Animate( - "opacity", Rml::Property(0.f, Rml::Unit::NUMBER), DAMAGE_ARC_FADE_SECONDS, - tween, 1, false, 0.f, &opaque); - } - - void set_aim_point(const std::optional normalized) override { - if (!normalized) { - reticle_->SetProperty( - Rml::PropertyId::Visibility, Rml::Property(Rml::Style::Visibility::Hidden)); - return; - } - reticle_->SetProperty( - Rml::PropertyId::Visibility, Rml::Property(Rml::Style::Visibility::Visible)); - reticle_->SetProperty( - Rml::PropertyId::Left, - Rml::Property(normalized->x * static_cast(width_), Rml::Unit::PX)); - reticle_->SetProperty( - Rml::PropertyId::Top, - Rml::Property(normalized->y * static_cast(height_), Rml::Unit::PX)); - } - - void render_hud_overlay() override { - context_->Update(); - - backend_->begin_ui_overlay(width_, height_); - context_->Render(); - backend_->end_ui_frame(); - } - - std::shared_ptr pause_input() override { - return input_adapter_; - } - - std::shared_ptr pause_gamepad_input() override { - return input_adapter_; - } - - void on_window_resized(const int width, const int height) override { - width_ = width; - height_ = height; - context_->SetDimensions(Rml::Vector2i(width_, height_)); - update_dp_ratio(); - } - - ~RmlGui() override { - // nothing may keep pointing at this object through the window - window_->set_keyboard_callback(nullptr); - window_->set_gamepad_callback(nullptr); - window_->set_resize_callback(nullptr); - - // releases the GL resources through the backend's render - // interface, whose context is still current on this thread - Rml::Shutdown(); - } - - private: - // Every dp length in menu.rcss is mapped to pixels relative to a - // 1080p design baseline, measured against the monitor the window - // sits on — not the window itself — so the menu keeps the same - // physical size on the display whether the game is fullscreen or - // in a small window (a 4K TV renders it twice as large either - // way). The min of both axes keeps the design fitting on unusual - // ratios. - void update_dp_ratio() const { - const auto [screen_width, screen_height] = window_->screen_size(); - context_->SetDensityIndependentPixelRatio(std::max( - 0.5f, std::min( - static_cast(screen_width) / 1920.0f, - static_cast(screen_height) / 1080.0f))); - } - - // Registered with an explicit family/weight (the static TTFs carry - // per-weight legacy family names that would not match the RCSS - // font-family otherwise). The buffers must outlive Rml::Shutdown(). - void - load_fonts(const std::shared_ptr &asset_reader) { - struct FontSpec { - const char *path; - const char *family; - int weight; - }; - constexpr FontSpec MENU_FONTS[] = { - {.path = "font/Sora-Regular.ttf", .family = "Sora", .weight = 400}, - {.path = "font/Sora-SemiBold.ttf", .family = "Sora", .weight = 600}, - {.path = "font/Sora-Bold.ttf", .family = "Sora", .weight = 700}, - {.path = "font/IBMPlexMono-Regular.ttf", - .family = "IBM Plex Mono", - .weight = 400}, - {.path = "font/IBMPlexMono-Medium.ttf", - .family = "IBM Plex Mono", - .weight = 500}, - {.path = "font/IBMPlexMono-SemiBold.ttf", - .family = "IBM Plex Mono", - .weight = 600}, - }; - - font_buffers_.reserve(std::size(MENU_FONTS)); - for (const auto &[path, family, weight]: MENU_FONTS) { - font_buffers_.push_back(asset_reader->read_text(path)); - const auto &buffer = font_buffers_.back(); - Rml::LoadFontFace( - Rml::Span( - reinterpret_cast(buffer.data()), buffer.size()), - family, Rml::Style::FontStyle::Normal, - static_cast(weight)); - } - } - - void bind_data_model() { - Rml::DataModelConstructor constructor = context_->CreateDataModel("settings"); - if (!constructor) throw std::runtime_error("RmlUi data model creation failed"); - - constructor.RegisterArray>(); - - if (auto row_handle = constructor.RegisterStruct()) { - row_handle.RegisterMember("label", &BindingRow::label); - row_handle.RegisterMember("binding", &BindingRow::binding); - row_handle.RegisterMember("listening", &BindingRow::listening); - row_handle.RegisterMember("bound", &BindingRow::bound); - } - constructor.RegisterArray>(); - - constructor.Bind("kb_rows", &kb_rows_); - constructor.Bind("gp_rows", &gp_rows_); - constructor.Bind("gamepads", &gamepad_names_); - constructor.Bind("selected_gamepad", &selected_gamepad_); - constructor.Bind("bind_status", &bind_status_); - constructor.Bind("bind_warning", &bind_warning_); - - constructor.Bind("nb_tanks", &settings_.nb_tanks); - constructor.Bind("spawn_side", &settings_.spawn_side); - constructor.Bind("controller", &controller_display_); - constructor.Bind("display", &display_display_); - constructor.Bind("shadow_quality", &shadow_display_); - constructor.Bind("msaa", &settings_.msaa_samples); - constructor.Bind("gpus", &gpu_names_); - constructor.Bind("selected_window_gpu", &selected_window_gpu_); - constructor.Bind("selected_vision_gpu", &selected_vision_gpu_); - constructor.Bind("window_gpu_env", &window_gpu_env_override_); - constructor.Bind("vision_gpu_env", &vision_gpu_env_override_); - constructor.Bind("sac_folder", &sac_folder_display_); - constructor.Bind("sac_status", &sac_status_); - constructor.Bind("sac_valid", &sac_valid_); - constructor.Bind("current_dir", ¤t_dir_display_); - constructor.Bind("entries", &entries_); - constructor.Bind("can_play", &can_play_); - constructor.Bind("score", &score_); - - constructor.BindEventCallback( - "play", [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - if (can_play_) play_clicked_ = true; - }); - constructor.BindEventCallback( - "exit", [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - quit_clicked_ = true; - }); - constructor.BindEventCallback( - "open_params", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - main_document_->Hide(); - params_document_->Show(); - }); - constructor.BindEventCallback( - "back", [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - close_params(); - }); - constructor.BindEventCallback( - "enter_dir", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { - if (arguments.empty()) return; - const auto index = static_cast(arguments[0].Get()); - if (index >= entries_.size()) return; - - const std::string &entry = entries_[index]; - current_dir_ = - entry == ".." ? current_dir_.parent_path() : current_dir_ / entry; - refresh_explorer(); - focus_explorer_pending_ = true; - }); - constructor.BindEventCallback( - "set_controller", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { - if (arguments.empty()) return; - controller_display_ = arguments[0].Get(); - settings_.controller_kind = controller_display_ == "gamepad" - ? ControllerKind::Gamepad - : ControllerKind::Keyboard; - model_handle_.DirtyVariable("controller"); - }); - constructor.BindEventCallback( - "set_display", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { - if (arguments.empty()) return; - display_display_ = arguments[0].Get(); - settings_.fullscreen = display_display_ == "fullscreen"; - // applied immediately; the window reports its new size - // through the resize callback (dp-ratio included) - window_->set_fullscreen(settings_.fullscreen); - model_handle_.DirtyVariable("display"); - }); - constructor.BindEventCallback( - "open_graphics", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - params_document_->Hide(); - graphics_document_->Show(); - }); - constructor.BindEventCallback( - "graphics_back", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - close_graphics(); - }); - constructor.BindEventCallback( - "set_shadow_quality", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { - if (arguments.empty()) return; - const auto quality = - shadow_quality_from_string(arguments[0].Get()); - if (!quality) return; - settings_.shadow_quality = *quality; - shadow_display_ = to_string(*quality); - model_handle_.DirtyVariable("shadow_quality"); - }); - constructor.BindEventCallback( - "set_msaa", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { - if (arguments.empty()) return; - const int samples = arguments[0].Get(); - if (samples != 1 && samples != 2 && samples != 4 && samples != 8) return; - settings_.msaa_samples = samples; - model_handle_.DirtyVariable("msaa"); - }); - constructor.BindEventCallback( - "set_window_gpu", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { - if (arguments.empty()) return; - select_gpu( - arguments[0].Get(), selected_window_gpu_, settings_.window_gpu, - "selected_window_gpu"); - }); - constructor.BindEventCallback( - "set_vision_gpu", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { - if (arguments.empty()) return; - select_gpu( - arguments[0].Get(), selected_vision_gpu_, settings_.vision_gpu, - "selected_vision_gpu"); - }); - constructor.BindEventCallback( - "open_controls", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - open_controls(); - }); - constructor.BindEventCallback( - "controls_back", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - close_controls(); - }); - constructor.BindEventCallback( - "capture_kb", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { - if (!arguments.empty()) begin_capture(true, arguments[0].Get()); - }); - constructor.BindEventCallback( - "capture_gp", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { - if (!arguments.empty()) begin_capture(false, arguments[0].Get()); - }); - constructor.BindEventCallback( - "select_pad", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &arguments) { - if (arguments.empty()) return; - const auto index = static_cast(arguments[0].Get()); - if (index >= gamepads_.size()) return; - - settings_.bindings.gamepad.device_guid = gamepads_[index].guid; - settings_.bindings.gamepad.device_name = gamepads_[index].name; - window_->select_gamepad(gamepads_[index].id); - selected_gamepad_ = static_cast(index); - model_handle_.DirtyVariable("selected_gamepad"); - }); - constructor.BindEventCallback( - "reset_bindings", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - end_capture(); - if (settings_.controller_kind == ControllerKind::Keyboard) - settings_.bindings.keyboard = {}; - else { - // the device choice is not a binding: keep it - auto gamepad = GamepadBindings{}; - gamepad.device_guid = std::move(settings_.bindings.gamepad.device_guid); - gamepad.device_name = std::move(settings_.bindings.gamepad.device_name); - settings_.bindings.gamepad = std::move(gamepad); - } - set_default_bind_status(); - rebuild_binding_rows(); - }); - constructor.BindEventCallback( - "select_folder", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - settings_.sac_folder = current_dir_; - validate_sac_folder(); - refresh_explorer(); - }); - - constructor.BindEventCallback( - "pause_continue", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - pending_pause_action_ = PauseAction::Continue; - }); - constructor.BindEventCallback( - "pause_main_menu", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - pending_pause_action_ = PauseAction::MainMenu; - }); - constructor.BindEventCallback( - "pause_exit", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - pending_pause_action_ = PauseAction::ExitGame; - }); - constructor.BindEventCallback( - "game_over_retry", - [this](Rml::DataModelHandle, Rml::Event &, const Rml::VariantList &) { - pending_pause_action_ = PauseAction::Retry; - }); - - model_handle_ = constructor.GetModelHandle(); - } - - void refresh_explorer() { - entries_.clear(); - if (current_dir_.has_parent_path() && current_dir_ != current_dir_.root_path()) - entries_.emplace_back(".."); - - std::error_code list_error; - for (const auto &entry: - std::filesystem::directory_iterator(current_dir_, list_error)) - if (std::error_code type_error; entry.is_directory(type_error)) - entries_.push_back(entry.path().filename().string()); - if (list_error) - std::cerr << "Cannot list " << current_dir_ << ": " << list_error.message() - << std::endl; - - // keep ".." pinned first, sort the actual directories - const auto first_dir = - entries_.begin() + (!entries_.empty() && entries_[0] == ".." ? 1 : 0); - std::sort(first_dir, entries_.end()); - - current_dir_display_ = current_dir_.string(); - sac_folder_display_ = settings_.sac_folder.string(); - - if (model_handle_) { - model_handle_.DirtyVariable("entries"); - model_handle_.DirtyVariable("current_dir"); - model_handle_.DirtyVariable("sac_folder"); - model_handle_.DirtyVariable("sac_status"); - model_handle_.DirtyVariable("sac_valid"); - model_handle_.DirtyVariable("can_play"); - } - } - - // runs the injected dry-run load and turns its outcome into the - // tri-state the parameters screen displays (nothing chosen yet / - // model loaded / error message); can_play_ follows the real load - void validate_sac_folder() { - if (settings_.sac_folder.empty()) { - sac_valid_ = false; - sac_status_ = ""; - } else { - const auto error = sac_validator_(settings_.sac_folder); - sac_valid_ = !error.has_value(); - sac_status_ = error.value_or("AI model loaded"); - } - can_play_ = sac_valid_; - } - - void close_params() const { - params_document_->Hide(); - main_document_->Show(); - } - - // ---- graphics page ------------------------------------------ - - void close_graphics() const { - graphics_document_->Hide(); - params_document_->Show(); - } - - // "Auto" rides index 0 of gpu_names_, the actual devices follow - void - select_gpu(const int index, int &selected, std::string &setting, const char *variable) { - if (index < 0 || index >= static_cast(gpu_names_.size())) return; - selected = index; - setting = index == 0 ? std::string() : std::string(gpu_names_[index]); - model_handle_.DirtyVariable(variable); - } - - // ---- controls page ------------------------------------------ - - void open_controls() { - params_document_->Hide(); - refresh_gamepad_list(); - set_default_bind_status(); - rebuild_binding_rows(); - controls_document_->Show(); - } - - void close_controls() { - end_capture(); - rebuild_binding_rows(); - controls_document_->Hide(); - params_document_->Show(); - } - - std::array *, NB_KB_SLOTS> kb_slots() { - auto &keyboard = settings_.bindings.keyboard; - return { - &keyboard.forward, &keyboard.backward, &keyboard.turn_left, - &keyboard.turn_right, &keyboard.fire}; - } - - // gamepad slots 1..5 (0 is the fire button) - std::array *, NB_GP_SLOTS - 1> gp_axis_slots() { - auto &gamepad = settings_.bindings.gamepad; - return { - &gamepad.steer, &gamepad.aim_x, &gamepad.aim_y, &gamepad.accelerate, - &gamepad.reverse}; - } - - Rml::String keyboard_slot_label(const std::optional &slot) const { - if (!slot) return "UNBOUND"; - if (const auto *button = std::get_if(&*slot)) - switch (*button) { - case controller::MouseButton::Left: return "MOUSE L"; - case controller::MouseButton::Right: return "MOUSE R"; - case controller::MouseButton::Middle: return "MOUSE M"; - } - // layout-aware label (Key::Q reads "A" on AZERTY) - const auto label = window_->key_label(std::get(*slot)); - return label.empty() ? "?" : Rml::String(label); - } - - static Rml::String - axis_slot_label(const std::optional &slot, const bool one_way) { - if (!slot) return "UNBOUND"; - Rml::String label = GAMEPAD_AXIS_LABELS[static_cast(slot->axis)]; - // a one-way action bound to a stick reads a single direction - const bool on_stick = slot->axis != GamepadAxis::LeftTrigger - && slot->axis != GamepadAxis::RightTrigger; - if (one_way && on_stick) label += slot->sign > 0.f ? "+" : "-"; - return label; - } - - void rebuild_binding_rows() { - kb_rows_.resize(NB_KB_SLOTS); - const auto keyboard_slots = kb_slots(); - for (int i = 0; i < NB_KB_SLOTS; i++) { - const bool listening = capture_keyboard_page_ && capture_slot_ == i; - kb_rows_[i] = { - .label = KB_SLOT_LABELS[i], - .binding = - listening ? "PRESS A KEY..." : keyboard_slot_label(*keyboard_slots[i]), - .listening = listening, - .bound = keyboard_slots[i]->has_value()}; - } - - gp_rows_.resize(NB_GP_SLOTS); - const auto axis_slots = gp_axis_slots(); - for (int i = 0; i < NB_GP_SLOTS; i++) { - const bool listening = !capture_keyboard_page_ && capture_slot_ == i; - const bool fire_slot = i == 0; - Rml::String binding; - if (listening) binding = fire_slot ? "PRESS A BUTTON..." : "MOVE AN AXIS..."; - else if (fire_slot) - binding = settings_.bindings.gamepad.fire - ? controller::to_string(*settings_.bindings.gamepad.fire) - : "UNBOUND"; - else binding = axis_slot_label(*axis_slots[i - 1], gp_slot_is_one_way(i)); - gp_rows_[i] = { - .label = GP_SLOT_LABELS[i], - .binding = std::move(binding), - .listening = listening, - .bound = fire_slot ? settings_.bindings.gamepad.fire.has_value() - : axis_slots[i - 1]->has_value()}; - } - - if (model_handle_) { - model_handle_.DirtyVariable("kb_rows"); - model_handle_.DirtyVariable("gp_rows"); - model_handle_.DirtyVariable("bind_status"); - model_handle_.DirtyVariable("bind_warning"); - } - } - - void set_default_bind_status() { - bind_warning_ = false; - bind_status_ = settings_.controller_kind == ControllerKind::Keyboard - ? "Click a slot, then press the new key or mouse button. " - "Escape cancels." - : "Click a slot, then press a button or move an axis. " - "Escape or 15s of inactivity cancels — Start stays the " - "pause toggle."; - } - - void begin_capture(const bool keyboard_page, const int slot) { - const int nb_slots = keyboard_page ? NB_KB_SLOTS : NB_GP_SLOTS; - if (slot < 0 || slot >= nb_slots) return; - - capture_keyboard_page_ = keyboard_page; - capture_slot_ = slot; - capture_deadline_ = std::chrono::steady_clock::now() + CAPTURE_TIMEOUT; - // every axis must return to rest once before it can bind (the - // stick may still be deflected from navigating the menu) - axis_armed_.fill(false); - set_default_bind_status(); - - input_adapter_->set_capture_sink( - [this](const RawMenuInput &input) { on_capture_input(input); }); - rebuild_binding_rows(); - } - - void end_capture() { - capture_slot_ = -1; - input_adapter_->set_capture_sink(nullptr); - } - - void on_capture_input(const RawMenuInput &input) { - // Escape is the only cancel input (every pad button stays - // bindable); a pad-only player relies on the capture timeout - if (const auto *key = std::get_if(&input); - key != nullptr && *key == controller::Key::Escape) { - end_capture(); - rebuild_binding_rows(); - return; - } - - if (capture_keyboard_page_) { - if (const auto *key = std::get_if(&input)) - assign_keyboard(KeyboardBinding(*key)); - else if (const auto *button = std::get_if(&input)) - assign_keyboard(KeyboardBinding(*button)); - // pad input has no meaning on the keyboard page - return; - } - - if (capture_slot_ == 0) { - // Start stays the in-game pause toggle, never a binding - if (const auto *button = std::get_if(&input); - button != nullptr && *button != controller::GamepadButton::Start) - assign_gamepad_fire(*button); - return; - } - - if (const auto *motion = std::get_if>(&input)) { - const auto &[axis, value] = *motion; - auto &armed = axis_armed_[static_cast(axis)]; - if (std::abs(value) < CAPTURE_REST) armed = true; - else if (armed && std::abs(value) > CAPTURE_ENGAGE) - assign_gamepad_axis(axis, value); - } - } - - void unbind_conflict(const Rml::String &new_label, const Rml::String &old_label) { - bind_warning_ = true; - bind_status_ = new_label + " was bound to " + old_label + " — " + old_label - + " is now unbound."; - } - - void assign_keyboard(const KeyboardBinding &binding) { - const auto slots = kb_slots(); - *slots[capture_slot_] = binding; - - for (int i = 0; i < NB_KB_SLOTS; i++) - if (i != capture_slot_ && *slots[i] == binding) { - *slots[i] = std::nullopt; - unbind_conflict(keyboard_slot_label(binding), KB_SLOT_LABELS[i]); - } - - end_capture(); - rebuild_binding_rows(); - } - - void assign_gamepad_fire(const controller::GamepadButton button) { - // single button slot: no conflict possible - settings_.bindings.gamepad.fire = button; - end_capture(); - rebuild_binding_rows(); - } - - void assign_gamepad_axis(const GamepadAxis axis, const double value) { - const bool one_way = gp_slot_is_one_way(capture_slot_); - const auto slots = gp_axis_slots(); - const GamepadAxisBinding binding{ - .axis = axis, .sign = one_way && value < 0. ? -1.f : 1.f}; - *slots[capture_slot_ - 1] = binding; - - // two slots clash when they read the same range of an axis: a - // two-way slot owns the whole axis, one-way slots only their - // captured side - for (int slot = 1; slot < NB_GP_SLOTS; slot++) { - if (slot == capture_slot_) continue; - auto &other = *slots[slot - 1]; - if (!other || other->axis != axis) continue; - if (one_way && gp_slot_is_one_way(slot) && other->sign != binding.sign) - continue; - other = std::nullopt; - unbind_conflict(axis_slot_label(binding, one_way), GP_SLOT_LABELS[slot]); - } - - end_capture(); - rebuild_binding_rows(); - } - - void refresh_gamepad_list() { - auto gamepads = window_->list_gamepads(); - const bool changed = - gamepads.size() != gamepads_.size() - || !std::equal( - gamepads.begin(), gamepads.end(), gamepads_.begin(), - [](const view::GamepadInfo &a, const view::GamepadInfo &b) { - return a.id == b.id && a.guid == b.guid && a.name == b.name; - }); - if (!changed) return; - - gamepads_ = std::move(gamepads); - gamepad_names_.clear(); - for (const auto &pad: gamepads_) gamepad_names_.emplace_back(pad.name); - - // the preferred pad when connected, else the first one (which - // is what the window falls back to) - selected_gamepad_ = gamepads_.empty() ? -1 : 0; - for (size_t i = 0; i < gamepads_.size(); i++) - if (!settings_.bindings.gamepad.device_guid.empty() - && gamepads_[i].guid == settings_.bindings.gamepad.device_guid) { - selected_gamepad_ = static_cast(i); - break; - } - window_->select_gamepad( - selected_gamepad_ >= 0 ? gamepads_[selected_gamepad_].id : -1); - - if (model_handle_) { - model_handle_.DirtyVariable("gamepads"); - model_handle_.DirtyVariable("selected_gamepad"); - } - } - - void focus_first_entry() const { - const Rml::Element *list = params_document_->GetElementById("file-list"); - if (list == nullptr) return; - const auto entries = ExplorerNavListener::visible_file_entries(list); - if (entries.empty()) return; - if (entries.front()->Focus(true)) - entries.front()->ScrollIntoView(Rml::ScrollAlignment::Nearest); - } - - std::shared_ptr backend_; - std::shared_ptr window_; - - GameSettings settings_; - int width_; - int height_; - - MenuSystemInterface system_interface_; - ReaderBackedFileInterface file_interface_; - // unique_ptr: created after Rml::Initialise(), and member - // destruction keeps it alive until after Rml::Shutdown() as - // RmlUi requires of registered instancers - - std::unique_ptr cursor_ring_instancer_; - std::unique_ptr hit_marker_instancer_; - std::unique_ptr reticle_instancer_; - std::unique_ptr damage_arc_instancer_; - std::vector font_buffers_; - - Rml::Context *context_ = nullptr; - Rml::ElementDocument *main_document_ = nullptr; - Rml::ElementDocument *params_document_ = nullptr; - Rml::ElementDocument *controls_document_ = nullptr; - Rml::ElementDocument *graphics_document_ = nullptr; - Rml::ElementDocument *pause_document_ = nullptr; - Rml::ElementDocument *game_over_document_ = nullptr; - Rml::ElementDocument *hud_document_ = nullptr; - Rml::Element *reticle_ = nullptr; - Rml::Element *hit_marker_ = nullptr; - std::vector damage_arcs_; - std::size_t next_damage_arc_ = 0; - static constexpr float DAMAGE_ARC_FADE_SECONDS = 0.8f; - static constexpr float HIT_MARKER_FADE_SECONDS = 0.45f; - static constexpr float HIT_MARKER_END_SCALE = 1.3f; - static constexpr float KILL_MARKER_FADE_SECONDS = 0.6f; - static constexpr float KILL_MARKER_START_SCALE = 1.1f; - static constexpr float KILL_MARKER_END_SCALE = 1.55f; - Rml::DataModelHandle model_handle_; - - std::shared_ptr input_adapter_; - // removed from the document when Rml::Shutdown() destroys it in - // the destructor body, before the members are torn down - ExplorerNavListener explorer_nav_listener_; - bool focus_explorer_pending_ = false; - - std::filesystem::path current_dir_; - SacFolderValidator sac_validator_; - Rml::String controller_display_; - Rml::String display_display_; - // graphics page state - Rml::String shadow_display_; - std::vector gpu_names_; - int selected_window_gpu_ = 0; - int selected_vision_gpu_ = 0; - bool window_gpu_env_override_ = false; - bool vision_gpu_env_override_ = false; - Rml::String current_dir_display_; - Rml::String sac_folder_display_; - // empty while no folder is chosen; otherwise success or error text - Rml::String sac_status_; - std::vector entries_; - bool sac_valid_ = false; - bool can_play_ = false; - - // controls page state - std::vector kb_rows_; - std::vector gp_rows_; - std::vector gamepads_; - std::vector gamepad_names_; - // index into gamepads_ of the pad feeding the game, -1 when none - // is connected - int selected_gamepad_ = -1; - Rml::String bind_status_; - bool bind_warning_ = false; - // slot being captured (-1 = idle) and which page it belongs to - int capture_slot_ = -1; - bool capture_keyboard_page_ = false; - std::chrono::steady_clock::time_point capture_deadline_; - std::array axis_armed_{}; - // score shown by the pause and game-over popups - int score_ = 0; - bool play_clicked_ = false; - bool quit_clicked_ = false; - PauseAction pending_pause_action_ = PauseAction::None; - }; - - }// namespace - - std::unique_ptr make_gui( - const std::shared_ptr &backend, - const std::shared_ptr &asset_reader, - const GameSettings &initial_settings, const std::vector &gpus, - const int window_width, const int window_height, SacFolderValidator sac_validator) { - return std::make_unique( - backend, asset_reader, initial_settings, gpus, window_width, window_height, - std::move(sac_validator)); - } - -}// namespace arenai::desktop::gui diff --git a/arenai_desktop/src/main.cpp b/arenai_desktop/src/main.cpp index 188a3e54..c53feb72 100644 --- a/arenai_desktop/src/main.cpp +++ b/arenai_desktop/src/main.cpp @@ -3,7 +3,6 @@ // #include -#include #include @@ -29,63 +28,25 @@ static std::filesystem::path executable_dir(const char *argv0) { return std::filesystem::absolute(argv0).parent_path(); } -static void trim_inplace(std::string &s) { - auto not_space = [](const unsigned char c) { return !std::isspace(c); }; - s.erase(s.begin(), std::ranges::find_if(s, not_space)); - s.erase(std::find_if(s.rbegin(), s.rend(), not_space).base(), s.end()); -} - -static std::tuple parse_key_value(const std::string &value) { - const std::regex regex_match(R"(^ *([^=]+)=\"?([^"]+)\"? *$)"); - - std::smatch match; - - if (!std::regex_match(value, match, regex_match)) - throw std::invalid_argument("invalid pairs format, usage : key=value"); - - std::string s1 = match[1].str(); - std::string s2 = match[2].str(); - trim_inplace(s2); - - return {s1, s2}; -} - -typedef std::vector> hyper_params_vector; - int main(const int argc, char **argv) { argparse::ArgumentParser parser("arenai game"); - // Game options (nb_tanks, spawn_side and controller_kind are set in the menu) - parser.add_argument("--wanted_frequency").scan<'g', float>().default_value(1.f / 30.f); - parser.add_argument("--vision_height").scan<'i', int>().default_value(128); - parser.add_argument("--vision_width").scan<'i', int>().default_value(256); + // everything about the AI model (vision size, frequency, hyper-parameters) + // comes from the config.json selected in the menu; the command line only + // keeps what belongs to the player's machine parser.add_argument("--window_width").scan<'i', int>().default_value(1920); parser.add_argument("--window_height").scan<'i', int>().default_value(1080); - - // Model options - parser.add_argument("--hp", "--hyper_parameters") - .action(parse_key_value) - .append() - .default_value({}); parser.add_argument("--cuda").implicit_value(true).default_value(false); parser.parse_args(argc, argv); - std::map hyper_params; - for (const auto &[key, value]: parser.get("--hyper_parameters")) - hyper_params[key] = value; - const auto resources_folder = executable_dir(argv[0]) / "resources"; run_gui( - {.wanted_frequency = parser.get("--wanted_frequency"), - .window_width = parser.get("--window_width"), + {.window_width = parser.get("--window_width"), .window_height = parser.get("--window_height"), .resources_folder = resources_folder}, - {.vision_height = parser.get("--vision_height"), - .vision_width = parser.get("--vision_width"), - .hyper_parameters = hyper_params, - .state_dict_folder = resources_folder / "trained_models" / "ppo_train_386" / "save_66", + {.state_dict_folder = resources_folder / "trained_models" / "ppo_train_386" / "save_66", .cuda = parser.get("--cuda")}); return 0; diff --git a/resources/menu/ai.rml b/resources/menu/ai.rml new file mode 100644 index 00000000..ad988716 --- /dev/null +++ b/resources/menu/ai.rml @@ -0,0 +1,42 @@ + + + AI + + + +
+
+
←
+

Configure AI

+
+ +
+ Algorithm +
+
SAC
+
PPO
+
PPO Liquid
+
+
+ +

Trained AI model

+
{{current_dir}}
+
+
{{it}}
+
+
State dicts: {{agent_folder}}
+
Config: {{agent_config}}
+

{{agent_status}}

+

{{agent_status}}

+

No folder selected yet

+ +
+
Use this folder
+
+ +
+
Back
+
+
+ + diff --git a/resources/menu/controls.rml b/resources/menu/controls.rml index a522b270..540b3c01 100644 --- a/resources/menu/controls.rml +++ b/resources/menu/controls.rml @@ -10,6 +10,14 @@

Controls

+
+ Input +
+
Mouse + Keyboard
+
Gamepad
+
+
+

Mouse + Keyboard

diff --git a/resources/menu/graphics.rml b/resources/menu/graphics.rml index 03dd6856..5491c2c9 100644 --- a/resources/menu/graphics.rml +++ b/resources/menu/graphics.rml @@ -10,6 +10,14 @@

Graphics

+
+ Display +
+
Windowed
+
Fullscreen
+
+
+
Shadows
diff --git a/resources/menu/menu.rcss b/resources/menu/menu.rcss index 4de2ae1f..559f928f 100644 --- a/resources/menu/menu.rcss +++ b/resources/menu/menu.rcss @@ -207,6 +207,15 @@ h2 { margin-top: 14dp; } +/* dry-run load verdict of the AI page */ +.hint.status-ok { + color: #35D0A5; +} + +.hint.status-error { + color: #FF6B6B; +} + .param-row { display: block; margin: 16dp 0; @@ -331,31 +340,22 @@ input.range sliderarrowdec, input.range sliderarrowinc { border-color: #00A6FB; } -/* Controls and Graphics pages — same design width as the parameters panel */ -#controls-panel, #graphics-panel { +/* Controls, Graphics and AI pages — same design width as the parameters panel */ +#controls-panel, #graphics-panel, #ai-panel { width: 560dp; } -/* the Controls row lays its toggles out as a flex line so Configure can - ride its auto margin to the right edge */ -.toggle-group.controls-row { - display: flex; - align-items: center; +/* the three sub-page buttons of the parameters panel: centered by the base + .button auto margins, and the same visible size as the Back button below + (220dp content + 40dp padding + 2dp border) — plain, only Back keeps the + primary highlight. The group stands clear of the sliders above it. */ +.configure-group { + display: block; + margin-top: 32dp; } -/* opens the controls page from the Controls row: chip-sized, pushed to the - right edge by its auto margin */ .button.configure { - display: inline-block; - width: auto; - margin: 0 0 0 auto; - padding: 9dp 18dp; - font-size: 13dp; -} - -/* the Graphics row's Configure sits flush left under its label */ -.button.configure.left { - margin-left: 0; + width: 220dp; } /* binding rows: a dark inset list, like the file explorer */ diff --git a/resources/menu/parameters.rml b/resources/menu/parameters.rml index 55cf472b..9357c01c 100644 --- a/resources/menu/parameters.rml +++ b/resources/menu/parameters.rml @@ -20,41 +20,12 @@
-
- Controls -
-
Mouse + Keyboard
-
Gamepad
-
Configure
-
-
- -
- Display -
-
Windowed
-
Fullscreen
-
-
- -
- Graphics -
Configure
-
- -

Trained AI model folder

-
{{current_dir}}
-
-
{{it}}
-
-
Selected: {{sac_folder}}
-

{{sac_status}}

-

{{sac_status}}

-

No folder selected yet

- -
-
Use this folder
+
+
Configure Graphics
+
Configure Controls
+
Configure AI
+

The AI model is not loaded yet — configure it before playing.

Back
From 406f91e0d3aab915c25824272f6e2e3345699f33 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 6 Sep 2026 22:37:27 +0200 Subject: [PATCH 16/57] refactor parameters menu --- arenai_desktop/src/core/agent_loading_checker.cpp | 4 ++-- arenai_desktop/src/game.cpp | 5 +++-- arenai_desktop/src/game.h | 1 + arenai_desktop/src/gui/menu.h | 2 +- arenai_desktop/src/main.cpp | 1 + 5 files changed, 8 insertions(+), 5 deletions(-) diff --git a/arenai_desktop/src/core/agent_loading_checker.cpp b/arenai_desktop/src/core/agent_loading_checker.cpp index 32fdb38b..3aa3a0e3 100644 --- a/arenai_desktop/src/core/agent_loading_checker.cpp +++ b/arenai_desktop/src/core/agent_loading_checker.cpp @@ -15,8 +15,8 @@ namespace arenai::desktop { agent::AgentAlgorithm to_agent_algorithm(const gui::AiAlgorithm algorithm) { switch (algorithm) { case gui::AiAlgorithm::Sac: return agent::SAC; - case gui::AiAlgorithm::Ppo: return agent::PPO; - default: return agent::PPO_LIQUID; + case gui::AiAlgorithm::PpoLiquid: return agent::PPO_LIQUID; + default: return agent::PPO; } } diff --git a/arenai_desktop/src/game.cpp b/arenai_desktop/src/game.cpp index 3f261199..22bed8ef 100644 --- a/arenai_desktop/src/game.cpp +++ b/arenai_desktop/src/game.cpp @@ -189,8 +189,9 @@ namespace arenai::desktop { void run_gui(const GameOptions &game_options, const ModelOptions &model_options) { // loaded before the backend: the window GPU choice only applies at // device creation, i.e. here - const auto initial_settings = - load_preferences({.agent_folder = model_options.state_dict_folder}); + const auto initial_settings = load_preferences( + {.agent_folder = model_options.state_dict_folder, + .agent_config = model_options.config_json}); const std::shared_ptr graphics_backend = view::make_glfw_vulkan_backend( game_options.window_width, game_options.window_height, "ArenAI", diff --git a/arenai_desktop/src/game.h b/arenai_desktop/src/game.h index 99e353d5..b035734e 100644 --- a/arenai_desktop/src/game.h +++ b/arenai_desktop/src/game.h @@ -15,6 +15,7 @@ namespace arenai::desktop { // hyper-parameters) comes from the selected training run's config.json struct ModelOptions { std::filesystem::path state_dict_folder; + std::filesystem::path config_json; bool cuda; }; diff --git a/arenai_desktop/src/gui/menu.h b/arenai_desktop/src/gui/menu.h index bfdaaecf..e4e54744 100644 --- a/arenai_desktop/src/gui/menu.h +++ b/arenai_desktop/src/gui/menu.h @@ -101,7 +101,7 @@ namespace arenai::desktop::gui { // the algorithm to rebuild the networks with std::filesystem::path agent_folder; std::filesystem::path agent_config; - AiAlgorithm agent_algorithm = AiAlgorithm::PpoLiquid; + AiAlgorithm agent_algorithm = AiAlgorithm::Ppo; }; enum class MenuOutcome { Play, Quit }; diff --git a/arenai_desktop/src/main.cpp b/arenai_desktop/src/main.cpp index c53feb72..8f087a98 100644 --- a/arenai_desktop/src/main.cpp +++ b/arenai_desktop/src/main.cpp @@ -47,6 +47,7 @@ int main(const int argc, char **argv) { .window_height = parser.get("--window_height"), .resources_folder = resources_folder}, {.state_dict_folder = resources_folder / "trained_models" / "ppo_train_386" / "save_66", + .config_json = resources_folder / "trained_models" / "ppo_train_386" / "config.json", .cuda = parser.get("--cuda")}); return 0; From d6bd16a3092f0759981c0c5f148eeff35fce8486 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Wed, 9 Sep 2026 22:13:35 +0200 Subject: [PATCH 17/57] test : remove aim reward --- CLAUDE.md | 4 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 3 +- .../tests/src/tests_reward/tests_reward.cpp | 47 ++++++++++++++++--- 3 files changed, 44 insertions(+), 10 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index 3b7bfb10..e0f94a10 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -21,7 +21,9 @@ Dependency order: utils → model → view → core → agent / desktop - **arenai_view** — Vulkan rendering; `offscreen_renderer` = offscreen render for the agent's vision - **arenai_core** — `BaseTanksEnvironment`, enemy_handler, thread_pool (RL env loop) - **arenai_controller** — input handling -- **arenai_agent** — SAC: agents, networks, replay_buffer, reward_transforms + `main.cpp` (training executable) +- **arenai_agent** — agents (sac, ppo, ppo_liquid), networks, core (train env + spawn curriculum), + metrics + `main.cpp` (training executable). The reward is purely event-based + (hit/kill/received/death/win — no dense shaping). - **arenai_desktop** — the playable game executable. Its `src/` folders are hexagons of their own: `gui/` (RmlUi main menu — RmlUi types must never leak out of it, other code only includes `gui/menu.h`), `controller/`, `core/`. Menu assets live in `resources/menu/` + `resources/font/`. diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 33dcb1c2..9860e158 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -153,7 +153,6 @@ namespace arenai::model { const float aim_quality = compute_hit_reward( tracked.fire_pos, tracked.enemy_pos_at_t, tracked.shell_pos_at_t); - // pure bonus: firing must never be penalized, only rewarded when on target detail.aim += hit_reward_scale * aim_quality; detail.nb_landed_shells++; @@ -172,7 +171,7 @@ namespace arenai::model { // 4. total reward, kept split for the metrics last_reward_detail = detail; - return detail.aim + detail.hit + detail.received + detail.death; + return detail.hit + detail.received + detail.death; } RewardDetail JoltEnemyTank::get_last_reward_detail() const { return last_reward_detail; } diff --git a/arenai_model/tests/src/tests_reward/tests_reward.cpp b/arenai_model/tests/src/tests_reward/tests_reward.cpp index 0f5f80f2..12c0137e 100644 --- a/arenai_model/tests/src/tests_reward/tests_reward.cpp +++ b/arenai_model/tests/src/tests_reward/tests_reward.cpp @@ -34,10 +34,8 @@ TEST_F(RewardTest, RewardZeroWhenAliveNoShot) { const float reward_a = tanks[0]->get_reward(); const float reward_b = tanks[1]->get_reward(); - // the dense aim shaping leaves a negligible residue when the canon points ~90° - // away from the enemy, so the reward is near zero rather than exactly zero - ASSERT_NEAR(reward_a, 0.f, 1e-3f); - ASSERT_NEAR(reward_b, 0.f, 1e-3f); + ASSERT_FLOAT_EQ(reward_a, 0.f); + ASSERT_FLOAT_EQ(reward_b, 0.f); } TEST_F(RewardTest, RewardNegativeWhenDead) { @@ -179,7 +177,6 @@ TEST_F(RewardTest, RewardUnderOneAfterHit) { ASSERT_GE(max_reward_on_hit, 0.2f) << "reward should be greater than or equal to the hit bonus after hitting an enemy"; - // no fire, reward under the hit bonus constexpr user_input no_fire_input{ .left_joystick = {.x = 0.f, .y = 0.f}, .right_joystick = {.x = 0.f, .y = 0.f}, @@ -198,8 +195,44 @@ TEST_F(RewardTest, RewardUnderOneAfterHit) { ASSERT_FALSE(std::isnan(max_reward_on_no_hit)) << "reward should never be NaN"; ASSERT_FALSE(std::isinf(max_reward_on_no_hit)) << "reward should never be Inf"; - ASSERT_LE(max_reward_on_no_hit, 0.2f) - << "reward should stay under the hit bonus when no shell hit an enemy"; + ASSERT_FLOAT_EQ(max_reward_on_no_hit, 0.f) + << "reward should be exactly zero when no shell hit an enemy"; +} + +TEST_F(RewardTest, MissedShellPaysNothing) { + add_ground(); + auto tank_a = + tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank_b = + tank_factory->make_enemy_tank(file_reader, "tank_b", {15.f, 5.f, 30.f}, false, 60.f); + + for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); + + const std::shared_ptr shared_a(tank_a.release()); + const std::shared_ptr shared_b(tank_b.release()); + + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; + for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(fire_input); + + const std::vector tanks{shared_a, shared_b}; + + float total_reward = 0.f; + int max_landed_shells = 0; + for (int i = 0; i < 180; i++) { + engine->step(1.f / 60.f); + shared_a->tick(tanks); + + total_reward += shared_a->get_reward(); + max_landed_shells = + std::max(shared_a->get_last_reward_detail().nb_landed_shells, max_landed_shells); + } + + ASSERT_FALSE(shared_a->consume_has_hit()) << "shell should have missed the enemy tank"; + ASSERT_GT(max_landed_shells, 0) << "the shell tracker should have sampled a landed shell"; + ASSERT_FLOAT_EQ(total_reward, 0.f) << "a shell that lands without hitting must pay nothing"; } // ======================================================================== From eba44cef755f20ebbdb893ce2c860917d36e7e69 Mon Sep 17 00:00:00 2001 From: samuel Date: Wed, 9 Sep 2026 22:14:06 +0200 Subject: [PATCH 18/57] update claude.md # Conflicts: # CLAUDE.md --- CLAUDE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CLAUDE.md b/CLAUDE.md index e0f94a10..4df40beb 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -9,7 +9,7 @@ En cas de doute ou d'alternative de design : **poser la question d'abord**, prop options, attendre la réponse. Les corrections de bugs internes (fichiers `src/`) qui ne changent ni contrat ni comportement voulu restent autorisées. -A neural network (SAC) trained to play a tank-arena game. +A neural network (PPO) trained to play a tank-arena game. Jolt physics, offscreen Vulkan rendering (the agent's vision), NN via LibTorch. ## Architecture (CMake modules) From 9533c3080c435960c06b48e84ca71705fe6e8b23 Mon Sep 17 00:00:00 2001 From: Samuel Date: Wed, 9 Sep 2026 23:28:59 +0200 Subject: [PATCH 19/57] fix windows build --- arenai_agent/src/networks/recurrent/liquid_actor.cpp | 2 +- arenai_agent/src/networks/recurrent/liquid_critic.cpp | 4 ++-- arenai_agent/src/networks/recurrent/liquid_critic.h | 2 +- arenai_agent/src/networks/recurrent/liquid_recurrent.cpp | 6 +++--- arenai_agent/src/utils/cli_parser.cpp | 4 ++-- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.cpp b/arenai_agent/src/networks/recurrent/liquid_actor.cpp index 2588eefa..d294180e 100644 --- a/arenai_agent/src/networks/recurrent/liquid_actor.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_actor.cpp @@ -87,7 +87,7 @@ namespace arenai::agent { std::vector outputs; outputs.reserve(nb_steps); - for (long t = 0; t < nb_steps; t++) { + for (auto t = 0; t < nb_steps; t++) { auto [output, next_x] = liquid->forward_step( x, encoded.index({at::indexing::Slice(), t, at::indexing::Slice()})); diff --git a/arenai_agent/src/networks/recurrent/liquid_critic.cpp b/arenai_agent/src/networks/recurrent/liquid_critic.cpp index 41d1168c..ab373af7 100644 --- a/arenai_agent/src/networks/recurrent/liquid_critic.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_critic.cpp @@ -63,7 +63,7 @@ namespace arenai::agent { std::vector outputs; outputs.reserve(nb_steps); - for (long t = 0; t < nb_steps; t++) { + for (auto t = 0; t < nb_steps; t++) { auto [output, next_x] = liquid->forward_step( x, encoded.index({at::indexing::Slice(), t, at::indexing::Slice()})); @@ -74,7 +74,7 @@ namespace arenai::agent { return {.value = to_value->forward(torch::stack(outputs, 1)), .next_x = x}; } - torch::Tensor LiquidCritic::initial_state(const int batch_size) { + torch::Tensor LiquidCritic::initial_state(const int batch_size) const { return liquid->get_first_x(batch_size); } diff --git a/arenai_agent/src/networks/recurrent/liquid_critic.h b/arenai_agent/src/networks/recurrent/liquid_critic.h index c61337d8..e513b908 100644 --- a/arenai_agent/src/networks/recurrent/liquid_critic.h +++ b/arenai_agent/src/networks/recurrent/liquid_critic.h @@ -39,7 +39,7 @@ namespace arenai::agent { LiquidCriticOutput value_sequence( const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t); - torch::Tensor initial_state(int batch_size); + torch::Tensor initial_state(int batch_size) const; private: std::shared_ptr vision_encoder; diff --git a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp b/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp index ee69b7f6..a2dcb63b 100644 --- a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp @@ -85,15 +85,15 @@ torch::Tensor LiquidRecurrent::forward(const torch::Tensor &inputs) { inputs.sizes().size() == 3, "Processed input needs to have 3 dimensions (Batch, Time, Features)"); - const long batch_size = inputs.size(0); - const long nb_steps = inputs.size(1); + const auto batch_size = inputs.size(0); + const auto nb_steps = inputs.size(1); auto x_t = get_first_x(static_cast(batch_size)); std::vector results; results.reserve(nb_steps); - for (long t = 0; t < nb_steps; t++) { + for (auto t = 0; t < nb_steps; t++) { auto [output, x_t_next] = forward_step(x_t, inputs.index({at::indexing::Slice(), t, at::indexing::Slice()})); diff --git a/arenai_agent/src/utils/cli_parser.cpp b/arenai_agent/src/utils/cli_parser.cpp index 0abc5a69..e9921fa8 100644 --- a/arenai_agent/src/utils/cli_parser.cpp +++ b/arenai_agent/src/utils/cli_parser.cpp @@ -13,7 +13,7 @@ namespace arenai::agent { vision_channels parse_cli_vision_channels(const std::string &value) { const std::regex regex_match( - R"(^ *\[(?: *\( *\d+ *, *\d+ *\) *,)* *\( *\d+ *, *\d+ *\) *] *$)"); + R"(^ *\[(?: *\( *\d+ *, *\d+ *\) *,)* *\( *\d+ *, *\d+ *\) *\] *$)"); const std::regex regex_layer(R"(\( *\d+ *, *\d+ *\))"); const std::regex regex_channel(R"(\d+)"); @@ -44,7 +44,7 @@ namespace arenai::agent { std::vector parse_int_vector( const std::string &value, const std::string &cli_arg_name, const std::string &cli_arg_value_suggestion) { - const std::regex regex_match(R"(^ *\[(?: *\d+ *,)* *\d+ *] *$)"); + const std::regex regex_match(R"(^ *\[(?: *\d+ *,)* *\d+ *\] *$)"); const std::regex regex_groups(R"(\d+)"); if (!std::regex_match(value.begin(), value.end(), regex_match)) From f684cc6208f8971aab5cae8d9b8d108f630da67b Mon Sep 17 00:00:00 2001 From: samuel Date: Fri, 11 Sep 2026 16:26:48 +0200 Subject: [PATCH 20/57] stash beta law --- .../agents/ppo_liquid/liquid_ppo_agent.cpp | 11 +- .../agents/ppo_liquid/liquid_ppo_trainer.cpp | 10 +- arenai_agent/src/distributions/beta_law.cpp | 31 ++++- arenai_agent/src/distributions/beta_law.h | 12 +- arenai_agent/src/networks/constants.h | 5 + arenai_agent/src/networks/misc.cpp | 20 +++ arenai_agent/src/networks/misc.h | 14 ++ .../src/networks/recurrent/liquid_actor.cpp | 29 +++-- .../src/networks/recurrent/liquid_actor.h | 10 +- arenai_agent/src/networks_utils/init.cpp | 22 ++++ arenai_agent/src/networks_utils/init.h | 1 + .../tests_distributions/tests_beta_law.cpp | 74 +++++++---- .../tests_beta_law_edge.cpp | 123 +++++++++--------- 13 files changed, 238 insertions(+), 124 deletions(-) diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp index 5e685b3d..d73609ae 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp @@ -4,8 +4,8 @@ #include "./liquid_ppo_agent.h" +#include "../../distributions/beta_law.h" #include "../../distributions/multinomial.h" -#include "../../distributions/truncated_normal.h" #include "../../networks/constants.h" #include "../../networks_utils/torch_converter.h" #include "../../networks_utils/torch_loader.h" @@ -46,20 +46,21 @@ namespace arenai::agent { const auto &[vision, sensors] = state; x_t = hidden_state->get(vision.size(0)); - const auto &[mu, sigma, discrete_proba, next_x] = actor->act(vision, sensors, x_t); + const auto &[mode, concentration, discrete_proba, next_x] = + actor->act(vision, sensors, x_t); hidden_state->set(next_x); if (sample) { - action.continuous_action = truncated_normal_sample(mu, sigma); + action.continuous_action = beta_law_sample(mode, concentration); action.discrete_action = multinomial_sample(discrete_proba); } else { - action.continuous_action = truncated_normal_mean(mu, sigma); + action.continuous_action = beta_law_mean_action(mode, concentration); action.discrete_action = multinomial_max_action(discrete_proba); } // old log-probabilities, kept for the PPO importance ratio continuous_log_prob = - truncated_normal_log_pdf(action.continuous_action, mu, sigma).sum(-1, true); + beta_law_log_proba(action.continuous_action, mode, concentration).sum(-1, true); const auto clamped_proba = torch::clamp(discrete_proba, EPSILON, 1.0 - EPSILON); discrete_log_prob = (action.discrete_action * torch::log(clamped_proba)).sum(-1, true); diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp index b853462c..2395ca17 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp @@ -7,8 +7,8 @@ #include #include +#include "../../distributions/beta_law.h" #include "../../distributions/multinomial.h" -#include "../../distributions/truncated_normal.h" #include "../../metrics/mean_metric.h" #include "../../networks/constants.h" #include "../../networks_utils/print_module.h" @@ -205,12 +205,12 @@ namespace arenai::agent { return tensor.flatten(0, 1).index_select(0, valid_idx); }; - const auto mu = rows(out.mu); - const auto sigma = rows(out.sigma); + const auto mode = rows(out.mode); + const auto concentration = rows(out.concentration); const auto discrete_proba = rows(out.discrete); const auto curr_continuous_log_probs = - truncated_normal_log_pdf(rows(continuous_actions), mu, sigma).sum(-1, true); + beta_law_log_proba(rows(continuous_actions), mode, concentration).sum(-1, true); const auto clamped_proba = torch::clamp(discrete_proba, EPSILON, 1.0 - EPSILON); const auto curr_discrete_log_probs = @@ -222,7 +222,7 @@ namespace arenai::agent { const auto ratio = torch::exp(log_ratio); - const auto continuous_entropy = truncated_normal_entropy(mu, sigma); + const auto continuous_entropy = beta_law_entropy(mode, concentration); const auto discrete_entropy = multinomial_entropy(discrete_proba); const auto kl_per_row = (ratio - 1.f - log_ratio).flatten(); diff --git a/arenai_agent/src/distributions/beta_law.cpp b/arenai_agent/src/distributions/beta_law.cpp index 96126655..94d9e4d9 100644 --- a/arenai_agent/src/distributions/beta_law.cpp +++ b/arenai_agent/src/distributions/beta_law.cpp @@ -13,13 +13,24 @@ namespace arenai::agent { // the log-proba and entropy carry the log(2) change of scale from [0, 1] to [-1, 1] + torch::Tensor to_alpha(const torch::Tensor &mode, const torch::Tensor &concentration) { + return mode * (concentration - 2.0) + 1.0; + } + + torch::Tensor to_beta(const torch::Tensor &mode, const torch::Tensor &concentration) { + return (1.0 - mode) * (concentration - 2.0) + 1.0; + } + static torch::Tensor clamp_pos(const torch::Tensor &t) { return torch::clamp_min(t, EPSILON); } static torch::Tensor log_beta_function(const torch::Tensor &alpha, const torch::Tensor &beta) { return torch::lgamma(alpha) + torch::lgamma(beta) - torch::lgamma(alpha + beta); } - torch::Tensor beta_law_sample(const torch::Tensor &alpha, const torch::Tensor &beta) { + torch::Tensor beta_law_sample(const torch::Tensor &mode, const torch::Tensor &concentration) { + const auto alpha = to_alpha(mode, concentration); + const auto beta = to_beta(mode, concentration); + // Beta(α, β) = X / (X + Y) with X ~ Gamma(α, 1) and Y ~ Gamma(β, 1), // differentiable w.r.t. α and β through the implicit gradients of _standard_gamma const auto x = at::_standard_gamma(clamp_pos(alpha)); @@ -30,7 +41,10 @@ namespace arenai::agent { } torch::Tensor beta_law_log_proba( - const torch::Tensor &x, const torch::Tensor &alpha, const torch::Tensor &beta) { + const torch::Tensor &x, const torch::Tensor &mode, const torch::Tensor &concentration) { + const auto alpha = to_alpha(mode, concentration); + const auto beta = to_beta(mode, concentration); + const auto clamped_alpha = clamp_pos(alpha); const auto clamped_beta = clamp_pos(beta); @@ -41,7 +55,10 @@ namespace arenai::agent { - log_beta_function(clamped_alpha, clamped_beta) - std::log(2.0); } - torch::Tensor beta_law_entropy(const torch::Tensor &alpha, const torch::Tensor &beta) { + torch::Tensor beta_law_entropy(const torch::Tensor &mode, const torch::Tensor &concentration) { + const auto alpha = to_alpha(mode, concentration); + const auto beta = to_beta(mode, concentration); + const auto clamped_alpha = clamp_pos(alpha); const auto clamped_beta = clamp_pos(beta); @@ -52,7 +69,11 @@ namespace arenai::agent { + std::log(2.0); } - torch::Tensor beta_law_mean_action(const torch::Tensor &alpha, const torch::Tensor &beta) { + torch::Tensor + beta_law_mean_action(const torch::Tensor &mode, const torch::Tensor &concentration) { + const auto alpha = to_alpha(mode, concentration); + const auto beta = to_beta(mode, concentration); + const auto clamped_alpha = clamp_pos(alpha); const auto clamped_beta = clamp_pos(beta); @@ -60,7 +81,7 @@ namespace arenai::agent { } float beta_law_target_entropy(const int &nb_actions) { - return beta_law_entropy(torch::tensor(1.f), torch::tensor(1.f)).item() + return beta_law_entropy(torch::tensor(0.5f), torch::tensor(2.f)).item() * static_cast(nb_actions); } diff --git a/arenai_agent/src/distributions/beta_law.h b/arenai_agent/src/distributions/beta_law.h index a4ea4716..75a690f3 100644 --- a/arenai_agent/src/distributions/beta_law.h +++ b/arenai_agent/src/distributions/beta_law.h @@ -11,12 +11,16 @@ namespace arenai::agent { // Beta distribution rescaled to the [-1, 1] action support - torch::Tensor beta_law_sample(const torch::Tensor &alpha, const torch::Tensor &beta); + torch::Tensor to_alpha(const torch::Tensor &mode, const torch::Tensor &concentration); + torch::Tensor to_beta(const torch::Tensor &mode, const torch::Tensor &concentration); + + torch::Tensor beta_law_sample(const torch::Tensor &mode, const torch::Tensor &concentration); torch::Tensor beta_law_log_proba( - const torch::Tensor &x, const torch::Tensor &alpha, const torch::Tensor &beta); - torch::Tensor beta_law_entropy(const torch::Tensor &alpha, const torch::Tensor &beta); + const torch::Tensor &x, const torch::Tensor &mode, const torch::Tensor &concentration); + torch::Tensor beta_law_entropy(const torch::Tensor &mode, const torch::Tensor &concentration); - torch::Tensor beta_law_mean_action(const torch::Tensor &alpha, const torch::Tensor &beta); + torch::Tensor + beta_law_mean_action(const torch::Tensor &mode, const torch::Tensor &concentration); float beta_law_target_entropy(const int &nb_actions); diff --git a/arenai_agent/src/networks/constants.h b/arenai_agent/src/networks/constants.h index 2af7d3aa..5549665e 100644 --- a/arenai_agent/src/networks/constants.h +++ b/arenai_agent/src/networks/constants.h @@ -10,6 +10,11 @@ namespace arenai::agent { constexpr float SIGMA_MIN = 1e-4f; constexpr float SIGMA_MAX = 1.f; + + // Beta mode/concentration policy: κ ≥ 2 keeps the density unimodal (α, β ≥ 1); + // the floor sits just above the uniform, the ceiling caps the sharpness (σ ≈ 0.02) + constexpr float CONCENTRATION_MIN = 2.01f; + constexpr float CONCENTRATION_MAX = 2000.f; }// namespace arenai::agent #endif//ARENAI_CONSTANTS_H diff --git a/arenai_agent/src/networks/misc.cpp b/arenai_agent/src/networks/misc.cpp index 929aec17..3fed4dbf 100644 --- a/arenai_agent/src/networks/misc.cpp +++ b/arenai_agent/src/networks/misc.cpp @@ -50,4 +50,24 @@ namespace arenai::agent { << ", max=" << std::exp(max_log_sigma) << ")"; } + /* + * Concentration of Beta distribution output layer + */ + + ConcentrationOutput::ConcentrationOutput( + const float min_concentration, const float max_concentration) + : min_log_excess(std::log(min_concentration - 2.f)), + max_log_excess(std::log(max_concentration - 2.f)) {} + + torch::Tensor ConcentrationOutput::forward(const torch::Tensor &input) { + const auto log_excess = + min_log_excess + (max_log_excess - min_log_excess) * torch::sigmoid(input); + return 2.f + torch::exp(log_excess); + } + + void ConcentrationOutput::pretty_print(std::ostream &stream) { + stream << name() << "(min=" << 2.f + std::exp(min_log_excess) + << ", max=" << 2.f + std::exp(max_log_excess) << ")"; + } + }// namespace arenai::agent diff --git a/arenai_agent/src/networks/misc.h b/arenai_agent/src/networks/misc.h index 70daf773..b1fad429 100644 --- a/arenai_agent/src/networks/misc.h +++ b/arenai_agent/src/networks/misc.h @@ -49,6 +49,20 @@ namespace arenai::agent { float max_log_sigma; }; + // Beta concentration κ, log-scale on the excess κ - 2 bounded between min and max + class ConcentrationOutput : public AbstractFunctionModule { + public: + ConcentrationOutput(float min_concentration, float max_concentration); + + torch::Tensor forward(const torch::Tensor &input) override; + + void pretty_print(std::ostream &stream) override; + + private: + float min_log_excess; + float max_log_excess; + }; + }// namespace arenai::agent #endif//ARENAI_AGENT_HOST_MISC_H diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.cpp b/arenai_agent/src/networks/recurrent/liquid_actor.cpp index 2588eefa..c7dbd18f 100644 --- a/arenai_agent/src/networks/recurrent/liquid_actor.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_actor.cpp @@ -35,14 +35,15 @@ namespace arenai::agent { neuron_number, hidden_size_sensors + vision_encoder->get_output_size(), neuron_number, unfolding_steps, [](const torch::Tensor &t) { return torch::tanh(t); }, delta_t))), - mu(register_module( - "mu", + mode(register_module( + "mode", torch::nn::Sequential( - torch::nn::Linear(neuron_number, nb_continuous_actions), torch::nn::Tanh()))), - sigma(register_module( - "sigma", torch::nn::Sequential( - torch::nn::Linear(neuron_number, nb_continuous_actions), - std::make_shared(SIGMA_MIN, SIGMA_MAX)))), + torch::nn::Linear(neuron_number, nb_continuous_actions), torch::nn::Sigmoid()))), + concentration(register_module( + "concentration", + torch::nn::Sequential( + torch::nn::Linear(neuron_number, nb_continuous_actions), + std::make_shared(CONCENTRATION_MIN, CONCENTRATION_MAX)))), discrete(register_module( "discrete", torch::nn::Sequential( @@ -51,8 +52,10 @@ namespace arenai::agent { vision_encoder->apply(init_hidden_weights); sensors_encoder->apply(init_hidden_weights); - mu->apply(init_mu_output_weights); - sigma->apply([initial_sigma](Module &m) { init_sigma_output_weights(m, initial_sigma); }); + // zero bias + sigmoid puts the initial mode at the action-range center + mode->apply(init_mu_output_weights); + concentration->apply( + [initial_sigma](Module &m) { init_concentration_output_weights(m, initial_sigma); }); discrete->apply([initial_fire_proba](Module &m) { init_discrete_output_weights(m, initial_fire_proba); @@ -67,8 +70,8 @@ namespace arenai::agent { const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t) { const auto [output, next_x] = liquid->forward_step(x_t, encode(vision, sensors)); return { - .mu = mu->forward(output), - .sigma = sigma->forward(output), + .mode = mode->forward(output), + .concentration = concentration->forward(output), .discrete = discrete->forward(output), .next_x = next_x}; } @@ -97,8 +100,8 @@ namespace arenai::agent { const auto output = torch::stack(outputs, 1); return { - .mu = mu->forward(output), - .sigma = sigma->forward(output), + .mode = mode->forward(output), + .concentration = concentration->forward(output), .discrete = discrete->forward(output), .next_x = x}; } diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.h b/arenai_agent/src/networks/recurrent/liquid_actor.h index 546b6f75..54535160 100644 --- a/arenai_agent/src/networks/recurrent/liquid_actor.h +++ b/arenai_agent/src/networks/recurrent/liquid_actor.h @@ -15,8 +15,10 @@ namespace arenai::agent { struct LiquidActorOutput { - torch::Tensor mu; - torch::Tensor sigma; + // Beta distribution on [-1, 1]: mode in [0, 1] (on the underlying [0, 1] + // support) and concentration κ = α + β + torch::Tensor mode; + torch::Tensor concentration; torch::Tensor discrete; // liquid state after the last processed step torch::Tensor next_x; @@ -52,8 +54,8 @@ namespace arenai::agent { std::shared_ptr liquid; - torch::nn::Sequential mu; - torch::nn::Sequential sigma; + torch::nn::Sequential mode; + torch::nn::Sequential concentration; torch::nn::Sequential discrete; // [rows, features] recurrent input: encoded vision and sensors, concatenated diff --git a/arenai_agent/src/networks_utils/init.cpp b/arenai_agent/src/networks_utils/init.cpp index a15e3253..b2cbf5f7 100644 --- a/arenai_agent/src/networks_utils/init.cpp +++ b/arenai_agent/src/networks_utils/init.cpp @@ -4,6 +4,9 @@ #include "./init.h" +#include +#include + #include "../networks/constants.h" using namespace arenai; @@ -59,6 +62,25 @@ namespace arenai::agent { } } + void init_concentration_output_weights(torch::nn::Module &module, const float wanted_sigma) { + // Beta on [-1, 1]: var = 4 μ(1-μ) / (κ+1), so at μ = 0.5 a wanted action + // std σ maps to κ = 1/σ² - 1 + const auto wanted_concentration = std::clamp( + 1.f / (wanted_sigma * wanted_sigma) - 1.f, CONCENTRATION_MIN, CONCENTRATION_MAX); + + const float min_log_excess = std::log(CONCENTRATION_MIN - 2.f); + const float max_log_excess = std::log(CONCENTRATION_MAX - 2.f); + + const auto initial_sigmoid = (std::log(wanted_concentration - 2.f) - min_log_excess) + / (max_log_excess - min_log_excess); + const auto initial_logit = std::log(initial_sigmoid / (1.f - initial_sigmoid)); + + if (auto *lin = module.as()) { + torch::nn::init::orthogonal_(lin->weight, 0.01f); + if (lin->options.bias()) torch::nn::init::constant_(lin->bias, initial_logit); + } + } + void init_discrete_output_weights(torch::nn::Module &module, const float initial_fire_probability) { if (auto *lin = module.as()) { diff --git a/arenai_agent/src/networks_utils/init.h b/arenai_agent/src/networks_utils/init.h index 91d9c7af..b63c892a 100644 --- a/arenai_agent/src/networks_utils/init.h +++ b/arenai_agent/src/networks_utils/init.h @@ -15,6 +15,7 @@ namespace arenai::agent { void init_mu_output_weights(torch::nn::Module &module); void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma); + void init_concentration_output_weights(torch::nn::Module &module, float wanted_sigma); void init_discrete_output_weights(torch::nn::Module &module, float initial_fire_probability); void init_value_output_weights(torch::nn::Module &module); diff --git a/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp b/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp index 29aa0de8..40ee9761 100644 --- a/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp +++ b/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp @@ -14,13 +14,22 @@ using namespace arenai::agent; // ======================================================================== TEST_F(BetaLawTest, UniformEntropyIsMaximal) { - // alpha=1, beta=1 → uniform → maximal entropy - const auto entropy_uniform = beta_law_entropy(torch::tensor({1.0f}), torch::tensor({1.0f})); - const auto entropy_peaked = beta_law_entropy(torch::tensor({5.0f}), torch::tensor({5.0f})); + // concentration=2 → alpha=beta=1 → uniform → maximal entropy + const auto entropy_uniform = beta_law_entropy(torch::tensor({0.5f}), torch::tensor({2.0f})); + const auto entropy_peaked = beta_law_entropy(torch::tensor({0.5f}), torch::tensor({10.0f})); ASSERT_GT(entropy_uniform.item(), entropy_peaked.item()); } +TEST_F(BetaLawTest, EntropyDecreasesWithConcentration) { + const auto mode = torch::tensor({0.3f}); + + const auto entropy_low = beta_law_entropy(mode, torch::tensor({5.0f})); + const auto entropy_high = beta_law_entropy(mode, torch::tensor({50.0f})); + + ASSERT_GT(entropy_low.item(), entropy_high.item()); +} + TEST_F(BetaLawTest, TargetEntropyProportionalToActions) { const auto t1 = beta_law_target_entropy(1); const auto t3 = beta_law_target_entropy(3); @@ -29,15 +38,25 @@ TEST_F(BetaLawTest, TargetEntropyProportionalToActions) { } TEST_F(BetaLawTest, LogProbaConsistentWithSample) { - const auto alpha = torch::ones({100}) * 2.0f; - const auto beta = torch::ones({100}) * 3.0f; + const auto mode = torch::ones({100}) * 0.4f; + const auto concentration = torch::ones({100}) * 5.0f; - const auto samples = beta_law_sample(alpha, beta); - const auto log_p = beta_law_log_proba(samples, alpha, beta); + const auto samples = beta_law_sample(mode, concentration); + const auto log_p = beta_law_log_proba(samples, mode, concentration); ASSERT_TRUE(torch::all(torch::isfinite(log_p)).item()); } +TEST_F(BetaLawTest, MeanActionMatchesModeWhenCentered) { + // mode=0.5 → alpha=beta → mean action 0 on [-1, 1] + const auto mode = torch::ones({10}) * 0.5f; + const auto concentration = torch::ones({10}) * 8.0f; + + const auto mean = beta_law_mean_action(mode, concentration); + + ASSERT_TRUE(torch::allclose(mean, torch::zeros({10}), 1e-5, 1e-5)); +} + // ======================================================================== // Parameterized: shape variations // ======================================================================== @@ -45,12 +64,12 @@ TEST_F(BetaLawTest, LogProbaConsistentWithSample) { TEST_P(BetaLawParamTest, SampleBounds) { const auto &shape = GetParam(); - const auto alpha = torch::rand(shape) * 4.0f + 0.5f; - const auto beta = torch::rand(shape) * 4.0f + 0.5f; + const auto mode = torch::rand(shape); + const auto concentration = torch::rand(shape) * 8.0f + 2.0f; - const auto samples = beta_law_sample(alpha, beta); + const auto samples = beta_law_sample(mode, concentration); - ASSERT_EQ(samples.sizes(), alpha.sizes()); + ASSERT_EQ(samples.sizes(), mode.sizes()); ASSERT_TRUE(torch::all(torch::logical_and(torch::ge(samples, -1.0f), torch::le(samples, 1.0f))) .item()); } @@ -58,11 +77,11 @@ TEST_P(BetaLawParamTest, SampleBounds) { TEST_P(BetaLawParamTest, LogProbaShape) { const auto &shape = GetParam(); - const auto alpha = torch::rand(shape) * 4.0f + 0.5f; - const auto beta = torch::rand(shape) * 4.0f + 0.5f; - const auto samples = beta_law_sample(alpha, beta); + const auto mode = torch::rand(shape); + const auto concentration = torch::rand(shape) * 8.0f + 2.0f; + const auto samples = beta_law_sample(mode, concentration); - const auto log_p = beta_law_log_proba(samples, alpha, beta); + const auto log_p = beta_law_log_proba(samples, mode, concentration); ASSERT_EQ(log_p.sizes(), samples.sizes()); ASSERT_TRUE(torch::all(torch::isfinite(log_p)).item()); @@ -71,35 +90,36 @@ TEST_P(BetaLawParamTest, LogProbaShape) { TEST_P(BetaLawParamTest, EntropyShape) { const auto &shape = GetParam(); - const auto alpha = torch::rand(shape) * 4.0f + 0.5f; - const auto beta = torch::rand(shape) * 4.0f + 0.5f; + const auto mode = torch::rand(shape); + const auto concentration = torch::rand(shape) * 8.0f + 2.0f; - const auto entropy = beta_law_entropy(alpha, beta); + const auto entropy = beta_law_entropy(mode, concentration); - ASSERT_EQ(entropy.sizes(), alpha.sizes()); + ASSERT_EQ(entropy.sizes(), mode.sizes()); ASSERT_TRUE(torch::all(torch::isfinite(entropy)).item()); } -TEST_P(BetaLawParamTest, SampleNoNaNWithSmallParams) { +TEST_P(BetaLawParamTest, SampleNoNaNBelowUniformConcentration) { const auto &shape = GetParam(); - const auto alpha = torch::ones(shape) * 0.1f; - const auto beta = torch::ones(shape) * 0.1f; + // concentration below the κ=2 floor → alpha/beta below the clamp, still finite + const auto mode = torch::ones(shape) * 0.5f; + const auto concentration = torch::ones(shape) * 0.1f; - const auto samples = beta_law_sample(alpha, beta); + const auto samples = beta_law_sample(mode, concentration); ASSERT_TRUE(torch::all(torch::isfinite(samples)).item()); ASSERT_TRUE(torch::all(torch::logical_and(torch::ge(samples, -1.0f), torch::le(samples, 1.0f))) .item()); } -TEST_P(BetaLawParamTest, SampleNoNaNWithLargeParams) { +TEST_P(BetaLawParamTest, SampleNoNaNWithLargeConcentration) { const auto &shape = GetParam(); - const auto alpha = torch::ones(shape) * 50.0f; - const auto beta = torch::ones(shape) * 50.0f; + const auto mode = torch::ones(shape) * 0.5f; + const auto concentration = torch::ones(shape) * 2000.0f; - const auto samples = beta_law_sample(alpha, beta); + const auto samples = beta_law_sample(mode, concentration); ASSERT_TRUE(torch::all(torch::isfinite(samples)).item()); } diff --git a/arenai_agent/tests/src/tests_distributions/tests_beta_law_edge.cpp b/arenai_agent/tests/src/tests_distributions/tests_beta_law_edge.cpp index 457658c0..03f77aaf 100644 --- a/arenai_agent/tests/src/tests_distributions/tests_beta_law_edge.cpp +++ b/arenai_agent/tests/src/tests_distributions/tests_beta_law_edge.cpp @@ -13,44 +13,44 @@ using namespace arenai::agent; // Asymmetric parameter edge cases // ======================================================================== -TEST_F(BetaLawEdgeTest, VeryAsymmetricParamsAlphaLarge) { - const auto alpha = torch::ones({50}) * 100.0f; - const auto beta = torch::ones({50}) * 0.01f; +TEST_F(BetaLawEdgeTest, ModeNearUpperBound) { + const auto mode = torch::ones({50}) * 0.999f; + const auto concentration = torch::ones({50}) * 100.0f; - const auto samples = beta_law_sample(alpha, beta); - const auto log_p = beta_law_log_proba(samples, alpha, beta); - const auto entropy = beta_law_entropy(alpha, beta); + const auto samples = beta_law_sample(mode, concentration); + const auto log_p = beta_law_log_proba(samples, mode, concentration); + const auto entropy = beta_law_entropy(mode, concentration); ASSERT_TRUE(torch::all(torch::isfinite(samples)).item()) - << "Samples should be finite with alpha>>beta"; + << "Samples should be finite with mode near 1"; ASSERT_TRUE(torch::all(torch::isfinite(log_p)).item()) - << "Log-proba should be finite with alpha>>beta"; + << "Log-proba should be finite with mode near 1"; ASSERT_TRUE(torch::all(torch::isfinite(entropy)).item()) - << "Entropy should be finite with alpha>>beta"; + << "Entropy should be finite with mode near 1"; } -TEST_F(BetaLawEdgeTest, VeryAsymmetricParamsBetaLarge) { - const auto alpha = torch::ones({50}) * 0.01f; - const auto beta = torch::ones({50}) * 100.0f; +TEST_F(BetaLawEdgeTest, ModeNearLowerBound) { + const auto mode = torch::ones({50}) * 0.001f; + const auto concentration = torch::ones({50}) * 100.0f; - const auto samples = beta_law_sample(alpha, beta); - const auto log_p = beta_law_log_proba(samples, alpha, beta); - const auto entropy = beta_law_entropy(alpha, beta); + const auto samples = beta_law_sample(mode, concentration); + const auto log_p = beta_law_log_proba(samples, mode, concentration); + const auto entropy = beta_law_entropy(mode, concentration); ASSERT_TRUE(torch::all(torch::isfinite(samples)).item()) - << "Samples should be finite with beta>>alpha"; + << "Samples should be finite with mode near 0"; ASSERT_TRUE(torch::all(torch::isfinite(log_p)).item()) - << "Log-proba should be finite with beta>>alpha"; + << "Log-proba should be finite with mode near 0"; ASSERT_TRUE(torch::all(torch::isfinite(entropy)).item()) - << "Entropy should be finite with beta>>alpha"; + << "Entropy should be finite with mode near 0"; } TEST_F(BetaLawEdgeTest, ZeroParamsHandledGracefully) { - const auto alpha = torch::zeros({10}); - const auto beta = torch::zeros({10}); + const auto mode = torch::zeros({10}); + const auto concentration = torch::zeros({10}); - const auto samples = beta_law_sample(alpha, beta); - const auto entropy = beta_law_entropy(alpha, beta); + const auto samples = beta_law_sample(mode, concentration); + const auto entropy = beta_law_entropy(mode, concentration); ASSERT_TRUE(torch::all(torch::isfinite(samples)).item()) << "Samples should be finite with zero params (clamped to EPSILON)"; @@ -59,10 +59,10 @@ TEST_F(BetaLawEdgeTest, ZeroParamsHandledGracefully) { } TEST_F(BetaLawEdgeTest, NegativeParamsHandledGracefully) { - const auto alpha = torch::ones({10}) * -1.0f; - const auto beta = torch::ones({10}) * -1.0f; + const auto mode = torch::ones({10}) * -1.0f; + const auto concentration = torch::ones({10}) * -1.0f; - const auto samples = beta_law_sample(alpha, beta); + const auto samples = beta_law_sample(mode, concentration); ASSERT_TRUE(torch::all(torch::isfinite(samples)).item()) << "Samples should be finite with negative params (clamped to EPSILON)"; @@ -71,13 +71,13 @@ TEST_F(BetaLawEdgeTest, NegativeParamsHandledGracefully) { } TEST_F(BetaLawEdgeTest, LogProbAtBoundaryValues) { - const auto alpha = torch::ones({10}) * 2.0f; - const auto beta = torch::ones({10}) * 2.0f; + const auto mode = torch::ones({10}) * 0.5f; + const auto concentration = torch::ones({10}) * 6.0f; const auto x_near_minus1 = torch::ones({10}) * -0.999f; const auto x_near_plus1 = torch::ones({10}) * 0.999f; - const auto log_p_lo = beta_law_log_proba(x_near_minus1, alpha, beta); - const auto log_p_hi = beta_law_log_proba(x_near_plus1, alpha, beta); + const auto log_p_lo = beta_law_log_proba(x_near_minus1, mode, concentration); + const auto log_p_hi = beta_law_log_proba(x_near_plus1, mode, concentration); ASSERT_TRUE(torch::all(torch::isfinite(log_p_lo)).item()) << "Log-proba near -1 boundary should be finite"; @@ -86,13 +86,13 @@ TEST_F(BetaLawEdgeTest, LogProbAtBoundaryValues) { } TEST_F(BetaLawEdgeTest, LogProbAtExactBoundaryValues) { - const auto alpha = torch::ones({10}) * 2.0f; - const auto beta = torch::ones({10}) * 2.0f; + const auto mode = torch::ones({10}) * 0.5f; + const auto concentration = torch::ones({10}) * 6.0f; const auto x_minus1 = torch::ones({10}) * -1.0f; const auto x_plus1 = torch::ones({10}) * 1.0f; - const auto log_p_lo = beta_law_log_proba(x_minus1, alpha, beta); - const auto log_p_hi = beta_law_log_proba(x_plus1, alpha, beta); + const auto log_p_lo = beta_law_log_proba(x_minus1, mode, concentration); + const auto log_p_hi = beta_law_log_proba(x_plus1, mode, concentration); ASSERT_TRUE(torch::all(torch::isfinite(log_p_lo)).item()) << "Log-proba at exact -1 boundary should be finite (clamped)"; @@ -104,60 +104,61 @@ TEST_F(BetaLawEdgeTest, LogProbAtExactBoundaryValues) { // Gradient flow tests // ======================================================================== -TEST_F(BetaLawGradientTest, LogProbaGradientFlowsThroughAlpha) { - const auto alpha = torch::full({5}, 2.0f, torch::TensorOptions().requires_grad(true)); - const auto beta = torch::ones({5}) * 3.0f; +TEST_F(BetaLawGradientTest, LogProbaGradientFlowsThroughMode) { + const auto mode = torch::full({5}, 0.4f, torch::TensorOptions().requires_grad(true)); + const auto concentration = torch::ones({5}) * 6.0f; const auto x = torch::tensor({0.0f, 0.2f, -0.3f, 0.5f, -0.1f}); - const auto log_p = beta_law_log_proba(x, alpha, beta); + const auto log_p = beta_law_log_proba(x, mode, concentration); const auto loss = log_p.sum(); loss.backward(); - ASSERT_TRUE(alpha.grad().defined()) << "Gradient should flow back to alpha"; - ASSERT_TRUE(torch::all(torch::isfinite(alpha.grad())).item()) - << "Gradient w.r.t. alpha should be finite"; + ASSERT_TRUE(mode.grad().defined()) << "Gradient should flow back to mode"; + ASSERT_TRUE(torch::all(torch::isfinite(mode.grad())).item()) + << "Gradient w.r.t. mode should be finite"; } -TEST_F(BetaLawGradientTest, LogProbaGradientFlowsThroughBeta) { - const auto alpha = torch::ones({5}) * 2.0f; - const auto beta = torch::full({5}, 3.0f, torch::TensorOptions().requires_grad(true)); +TEST_F(BetaLawGradientTest, LogProbaGradientFlowsThroughConcentration) { + const auto mode = torch::ones({5}) * 0.4f; + const auto concentration = torch::full({5}, 6.0f, torch::TensorOptions().requires_grad(true)); const auto x = torch::tensor({0.0f, 0.2f, -0.3f, 0.5f, -0.1f}); - const auto log_p = beta_law_log_proba(x, alpha, beta); + const auto log_p = beta_law_log_proba(x, mode, concentration); const auto loss = log_p.sum(); loss.backward(); - ASSERT_TRUE(beta.grad().defined()) << "Gradient should flow back to beta"; - ASSERT_TRUE(torch::all(torch::isfinite(beta.grad())).item()) - << "Gradient w.r.t. beta should be finite"; + ASSERT_TRUE(concentration.grad().defined()) << "Gradient should flow back to concentration"; + ASSERT_TRUE(torch::all(torch::isfinite(concentration.grad())).item()) + << "Gradient w.r.t. concentration should be finite"; } -TEST_F(BetaLawGradientTest, EntropyGradientFlowsThroughAlpha) { - const auto alpha = torch::full({5}, 2.0f, torch::TensorOptions().requires_grad(true)); - const auto beta = torch::ones({5}) * 3.0f; +TEST_F(BetaLawGradientTest, EntropyGradientFlowsThroughMode) { + const auto mode = torch::full({5}, 0.4f, torch::TensorOptions().requires_grad(true)); + const auto concentration = torch::ones({5}) * 6.0f; - const auto entropy = beta_law_entropy(alpha, beta); + const auto entropy = beta_law_entropy(mode, concentration); const auto loss = entropy.sum(); loss.backward(); - ASSERT_TRUE(alpha.grad().defined()) << "Entropy gradient should flow back to alpha"; - ASSERT_TRUE(torch::all(torch::isfinite(alpha.grad())).item()) - << "Entropy gradient w.r.t. alpha should be finite"; + ASSERT_TRUE(mode.grad().defined()) << "Entropy gradient should flow back to mode"; + ASSERT_TRUE(torch::all(torch::isfinite(mode.grad())).item()) + << "Entropy gradient w.r.t. mode should be finite"; } -TEST_F(BetaLawGradientTest, EntropyGradientFlowsThroughBeta) { - const auto alpha = torch::ones({5}) * 2.0f; - const auto beta = torch::full({5}, 3.0f, torch::TensorOptions().requires_grad(true)); +TEST_F(BetaLawGradientTest, EntropyGradientFlowsThroughConcentration) { + const auto mode = torch::ones({5}) * 0.4f; + const auto concentration = torch::full({5}, 6.0f, torch::TensorOptions().requires_grad(true)); - const auto entropy = beta_law_entropy(alpha, beta); + const auto entropy = beta_law_entropy(mode, concentration); const auto loss = entropy.sum(); loss.backward(); - ASSERT_TRUE(beta.grad().defined()) << "Entropy gradient should flow back to beta"; - ASSERT_TRUE(torch::all(torch::isfinite(beta.grad())).item()) - << "Entropy gradient w.r.t. beta should be finite"; + ASSERT_TRUE(concentration.grad().defined()) + << "Entropy gradient should flow back to concentration"; + ASSERT_TRUE(torch::all(torch::isfinite(concentration.grad())).item()) + << "Entropy gradient w.r.t. concentration should be finite"; } From 8698f11c3f1c406ef3feaad21c3968e3d20b6eca Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sun, 13 Sep 2026 14:25:48 +0200 Subject: [PATCH 21/57] * fix hit ratio metric -> now per episode * lower target entropy --- .../ppo_liquid/liquid_ppo_hyperparams.h | 2 +- arenai_agent/src/core/train_environment.cpp | 20 +++++++++++-------- 2 files changed, 13 insertions(+), 9 deletions(-) diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h index 6909265d..8177ea92 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_hyperparams.h @@ -33,7 +33,7 @@ namespace arenai::agent { float clip_epsilon = 0.2f; float target_kl = 0.05f; float grad_norm_max = 0.5f; - float continuous_target_entropy = 0.4f; + float continuous_target_entropy = -0.88f; float discrete_target_entropy_factor = 0.2f; int epochs = 2; int rollout_size = 30 * 30; diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index 60606c0b..c3774317 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -42,7 +42,7 @@ namespace arenai::agent { miss_distance_metric(std::make_shared("miss", 1024 * nb_tanks, 1)), episode_step_mean_nb_metric(std::make_shared("s", 32, 1)), fire_metric(std::make_shared("fire", 256, 2)), - hit_metric(std::make_shared("hit", 256, 2, true)), + hit_metric(std::make_shared("hit", 16, 2, true)), kill_metric(std::make_shared("kill", 16, 1)), nb_kills_episode(0), nb_fires_episode(0), nb_hits_episode(0) {} @@ -102,7 +102,7 @@ namespace arenai::agent { return is_timeout_result; }); - // fire / hit frequencies (per second, per tank that acted this step) + // fire frequency (per second, per tank that acted this step) int nb_acting = 0, nb_fires = 0, nb_hits = 0; for (int i = 0; i < nb_tanks; i++) { if (already_done[i]) continue; @@ -111,12 +111,9 @@ namespace arenai::agent { nb_hits += has_hit[i] ? 1 : 0; } - if (nb_acting > 0) { + if (nb_acting > 0) fire_metric->add( static_cast(nb_fires) / (static_cast(nb_acting) * wanted_frequency)); - hit_metric->add( - static_cast(nb_hits) / (static_cast(nb_acting) * wanted_frequency)); - } nb_fires_episode += nb_fires; nb_hits_episode += nb_hits; @@ -182,8 +179,15 @@ namespace arenai::agent { void TrainTankEnvironment::on_reset_physics( const std::unique_ptr &engine) { - // close the previous episode's counter (skip the very first reset) - if (nb_steps > 0) kill_metric->add(static_cast(nb_kills_episode)); + // close the previous episode's counters (skip the very first reset) + if (nb_steps > 0) { + kill_metric->add(static_cast(nb_kills_episode)); + + // hit accuracy is undefined on an episode without a single fire + if (nb_fires_episode > 0) + hit_metric->add( + static_cast(nb_hits_episode) / static_cast(nb_fires_episode)); + } nb_kills_episode = 0; nb_steps = 0; From 67e127371fc4439f9b0f32e6d0cd4912714a4b6e Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sun, 13 Sep 2026 14:48:36 +0200 Subject: [PATCH 22/57] infer with beta mode for continuous action --- .../src/agents/ppo_liquid/liquid_ppo_agent.cpp | 2 +- arenai_agent/src/distributions/beta_law.cpp | 2 ++ arenai_agent/src/distributions/beta_law.h | 2 ++ .../tests/src/tests_distributions/tests_beta_law.cpp | 10 ++++++++++ 4 files changed, 15 insertions(+), 1 deletion(-) diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp index d73609ae..7f6cf4b1 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_agent.cpp @@ -54,7 +54,7 @@ namespace arenai::agent { action.continuous_action = beta_law_sample(mode, concentration); action.discrete_action = multinomial_sample(discrete_proba); } else { - action.continuous_action = beta_law_mean_action(mode, concentration); + action.continuous_action = beta_law_mode_action(mode); action.discrete_action = multinomial_max_action(discrete_proba); } diff --git a/arenai_agent/src/distributions/beta_law.cpp b/arenai_agent/src/distributions/beta_law.cpp index 94d9e4d9..d960d714 100644 --- a/arenai_agent/src/distributions/beta_law.cpp +++ b/arenai_agent/src/distributions/beta_law.cpp @@ -80,6 +80,8 @@ namespace arenai::agent { return 2.f * clamped_alpha / (clamped_alpha + clamped_beta) - 1.f; } + torch::Tensor beta_law_mode_action(const torch::Tensor &mode) { return mode * 2.f - 1.f; } + float beta_law_target_entropy(const int &nb_actions) { return beta_law_entropy(torch::tensor(0.5f), torch::tensor(2.f)).item() * static_cast(nb_actions); diff --git a/arenai_agent/src/distributions/beta_law.h b/arenai_agent/src/distributions/beta_law.h index 75a690f3..3e92feff 100644 --- a/arenai_agent/src/distributions/beta_law.h +++ b/arenai_agent/src/distributions/beta_law.h @@ -22,6 +22,8 @@ namespace arenai::agent { torch::Tensor beta_law_mean_action(const torch::Tensor &mode, const torch::Tensor &concentration); + torch::Tensor beta_law_mode_action(const torch::Tensor &mode); + float beta_law_target_entropy(const int &nb_actions); }// namespace arenai::agent diff --git a/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp b/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp index 40ee9761..3732e5ac 100644 --- a/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp +++ b/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp @@ -57,6 +57,16 @@ TEST_F(BetaLawTest, MeanActionMatchesModeWhenCentered) { ASSERT_TRUE(torch::allclose(mean, torch::zeros({10}), 1e-5, 1e-5)); } +TEST_F(BetaLawTest, ModeActionSpansActionRange) { + // mode in [0, 1] maps linearly to [-1, 1], independent of concentration + const auto mode = torch::tensor({0.0f, 0.25f, 0.5f, 0.75f, 1.0f}); + + const auto action = beta_law_mode_action(mode); + + ASSERT_TRUE( + torch::allclose(action, torch::tensor({-1.0f, -0.5f, 0.0f, 0.5f, 1.0f}), 1e-5, 1e-5)); +} + // ======================================================================== // Parameterized: shape variations // ======================================================================== From 97afe9c5bd1a71f20ee108a7d4bdafef4a5ab860 Mon Sep 17 00:00:00 2001 From: Samuel Date: Sun, 13 Sep 2026 21:56:26 +0200 Subject: [PATCH 23/57] fix windows build --- arenai_agent/src/networks/recurrent/liquid_critic.cpp | 4 ++-- arenai_agent/src/networks/recurrent/liquid_critic.h | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/arenai_agent/src/networks/recurrent/liquid_critic.cpp b/arenai_agent/src/networks/recurrent/liquid_critic.cpp index 41d1168c..ab373af7 100644 --- a/arenai_agent/src/networks/recurrent/liquid_critic.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_critic.cpp @@ -63,7 +63,7 @@ namespace arenai::agent { std::vector outputs; outputs.reserve(nb_steps); - for (long t = 0; t < nb_steps; t++) { + for (auto t = 0; t < nb_steps; t++) { auto [output, next_x] = liquid->forward_step( x, encoded.index({at::indexing::Slice(), t, at::indexing::Slice()})); @@ -74,7 +74,7 @@ namespace arenai::agent { return {.value = to_value->forward(torch::stack(outputs, 1)), .next_x = x}; } - torch::Tensor LiquidCritic::initial_state(const int batch_size) { + torch::Tensor LiquidCritic::initial_state(const int batch_size) const { return liquid->get_first_x(batch_size); } diff --git a/arenai_agent/src/networks/recurrent/liquid_critic.h b/arenai_agent/src/networks/recurrent/liquid_critic.h index c61337d8..e513b908 100644 --- a/arenai_agent/src/networks/recurrent/liquid_critic.h +++ b/arenai_agent/src/networks/recurrent/liquid_critic.h @@ -39,7 +39,7 @@ namespace arenai::agent { LiquidCriticOutput value_sequence( const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &x_t); - torch::Tensor initial_state(int batch_size); + torch::Tensor initial_state(int batch_size) const; private: std::shared_ptr vision_encoder; From 251dc4042c1ab2201db4d7dbfe21b4c170bb924e Mon Sep 17 00:00:00 2001 From: Samuel Date: Wed, 9 Sep 2026 23:28:59 +0200 Subject: [PATCH 24/57] fix windows build --- arenai_agent/src/networks/recurrent/liquid_actor.cpp | 2 +- arenai_agent/src/networks/recurrent/liquid_recurrent.cpp | 6 +++--- arenai_agent/src/utils/cli_parser.cpp | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.cpp b/arenai_agent/src/networks/recurrent/liquid_actor.cpp index c7dbd18f..8df5e05d 100644 --- a/arenai_agent/src/networks/recurrent/liquid_actor.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_actor.cpp @@ -90,7 +90,7 @@ namespace arenai::agent { std::vector outputs; outputs.reserve(nb_steps); - for (long t = 0; t < nb_steps; t++) { + for (auto t = 0; t < nb_steps; t++) { auto [output, next_x] = liquid->forward_step( x, encoded.index({at::indexing::Slice(), t, at::indexing::Slice()})); diff --git a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp b/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp index ee69b7f6..a2dcb63b 100644 --- a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp @@ -85,15 +85,15 @@ torch::Tensor LiquidRecurrent::forward(const torch::Tensor &inputs) { inputs.sizes().size() == 3, "Processed input needs to have 3 dimensions (Batch, Time, Features)"); - const long batch_size = inputs.size(0); - const long nb_steps = inputs.size(1); + const auto batch_size = inputs.size(0); + const auto nb_steps = inputs.size(1); auto x_t = get_first_x(static_cast(batch_size)); std::vector results; results.reserve(nb_steps); - for (long t = 0; t < nb_steps; t++) { + for (auto t = 0; t < nb_steps; t++) { auto [output, x_t_next] = forward_step(x_t, inputs.index({at::indexing::Slice(), t, at::indexing::Slice()})); diff --git a/arenai_agent/src/utils/cli_parser.cpp b/arenai_agent/src/utils/cli_parser.cpp index 0abc5a69..e9921fa8 100644 --- a/arenai_agent/src/utils/cli_parser.cpp +++ b/arenai_agent/src/utils/cli_parser.cpp @@ -13,7 +13,7 @@ namespace arenai::agent { vision_channels parse_cli_vision_channels(const std::string &value) { const std::regex regex_match( - R"(^ *\[(?: *\( *\d+ *, *\d+ *\) *,)* *\( *\d+ *, *\d+ *\) *] *$)"); + R"(^ *\[(?: *\( *\d+ *, *\d+ *\) *,)* *\( *\d+ *, *\d+ *\) *\] *$)"); const std::regex regex_layer(R"(\( *\d+ *, *\d+ *\))"); const std::regex regex_channel(R"(\d+)"); @@ -44,7 +44,7 @@ namespace arenai::agent { std::vector parse_int_vector( const std::string &value, const std::string &cli_arg_name, const std::string &cli_arg_value_suggestion) { - const std::regex regex_match(R"(^ *\[(?: *\d+ *,)* *\d+ *] *$)"); + const std::regex regex_match(R"(^ *\[(?: *\d+ *,)* *\d+ *\] *$)"); const std::regex regex_groups(R"(\d+)"); if (!std::regex_match(value.begin(), value.end(), regex_match)) From 2ab4fcb75ffaa9f0e7c4d8fb20fd5ff9b5ecf0aa Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sun, 13 Sep 2026 22:30:30 +0200 Subject: [PATCH 25/57] remove remaining steps from proprioception --- arenai_agent/src/core/train_environment.cpp | 2 +- arenai_core/include/arenai_core/environment.h | 3 +- arenai_core/src/environment.cpp | 9 ++-- arenai_core/tests/src/tests_environment.cpp | 28 +++++----- arenai_desktop/src/core/game_environment.cpp | 5 +- arenai_model/include/arenai_model/constants.h | 4 +- .../include/arenai_model/tank_factory.h | 3 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 9 +--- arenai_model/src/tank/jolt_enemy_tank.h | 3 +- arenai_model/src/tank/jolt_tank_factory.cpp | 5 +- arenai_model/src/tank/jolt_tank_factory.h | 4 +- .../src/tests_enemy_tank/tests_enemy_tank.cpp | 37 ++++++------- .../tests_player_tank/tests_player_tank.cpp | 5 +- .../tests_proprioception.cpp | 12 ++--- .../tests/src/tests_reward/tests_reward.cpp | 54 +++++++------------ .../tests/src/tests_shell/tests_shell.cpp | 45 ++++++---------- 16 files changed, 87 insertions(+), 141 deletions(-) diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index c3774317..4510fe91 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -27,7 +27,7 @@ namespace arenai::agent { : BaseTanksEnvironment( std::make_shared(android_assets_path), graphics_backend, nb_tanks, wanted_frequency, vision_height, vision_width, vision_num_threads, false, - true, static_cast(max_episode_steps) * wanted_frequency), + true), wanted_frequency(wanted_frequency), nb_tanks(nb_tanks), nb_steps(0), done(nb_tanks, false), already_done(nb_tanks, false), max_episode_steps(max_episode_steps), nb_hits_per_tanks(nb_tanks, 0), diff --git a/arenai_core/include/arenai_core/environment.h b/arenai_core/include/arenai_core/environment.h index 393a442d..bbc3464f 100644 --- a/arenai_core/include/arenai_core/environment.h +++ b/arenai_core/include/arenai_core/environment.h @@ -27,7 +27,7 @@ namespace arenai::core { const std::shared_ptr &file_reader, const std::shared_ptr &graphics_backend, int nb_tanks, float wanted_frequency, int vision_height, int vision_width, int vision_num_threads, - bool vision_thread_sleep, bool apply_timeout, float max_episode_seconds); + bool vision_thread_sleep, bool apply_timeout); virtual std::vector> step(float time_delta, const std::vector &actions); @@ -61,7 +61,6 @@ namespace arenai::core { bool drawing_started_; bool apply_timeout; - float max_episode_seconds; std::shared_ptr graphics_backend; std::shared_ptr gl_context; diff --git a/arenai_core/src/environment.cpp b/arenai_core/src/environment.cpp index d34f9f2f..1bd20ad4 100644 --- a/arenai_core/src/environment.cpp +++ b/arenai_core/src/environment.cpp @@ -18,8 +18,7 @@ namespace arenai::core { const std::shared_ptr &file_reader, const std::shared_ptr &graphics_backend, const int nb_tanks, float wanted_frequency, const int vision_height, const int vision_width, - const int vision_num_threads, const bool vision_thread_sleep, const bool apply_timeout, - const float max_episode_seconds) + const int vision_num_threads, const bool vision_thread_sleep, const bool apply_timeout) : wanted_frequency(wanted_frequency), nb_tanks(nb_tanks), vision_height(vision_height), vision_width(vision_width), vision_num_threads(vision_num_threads), vision_thread_sleep(vision_thread_sleep), @@ -29,8 +28,7 @@ namespace arenai::core { physic_engine(model::make_physic_engine(wanted_frequency)), nb_reset_frames(static_cast(4.f / wanted_frequency)), drawing_started_(false), apply_timeout(apply_timeout), graphics_backend(graphics_backend), - gl_context(graphics_backend->render_context()), rng(dev()), file_reader(file_reader), - max_episode_seconds(max_episode_seconds) {} + gl_context(graphics_backend->render_context()), rng(dev()), file_reader(file_reader) {} std::vector> BaseTanksEnvironment::step(const float time_delta, const std::vector &actions) { @@ -90,8 +88,7 @@ namespace arenai::core { for (int i = 0; i < nb_tanks; i++) { tanks.push_back(tank_factory->make_enemy_tank( file_reader, "enemy_" + std::to_string(i), - glm::vec3(x_pos_u_dist(rng), 0.f, y_pos_u_dist(rng)), apply_timeout, - max_episode_seconds)); + glm::vec3(x_pos_u_dist(rng), 0.f, y_pos_u_dist(rng)), apply_timeout)); tank_controller_handler.push_back(std::make_unique( wanted_frequency, model::ENEMY_TURRET_RADIAL_VELOCITY)); diff --git a/arenai_core/tests/src/tests_environment.cpp b/arenai_core/tests/src/tests_environment.cpp index 73c86d11..82c95acc 100644 --- a/arenai_core/tests/src/tests_environment.cpp +++ b/arenai_core/tests/src/tests_environment.cpp @@ -26,8 +26,7 @@ TEST_F(EnvironmentTest, ResetReturnsCorrectNumberOfStates) { constexpr int vision_w = 16; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false, - 60.f); + file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false); const auto states = env.reset(100.f, 100.f); @@ -47,8 +46,7 @@ TEST_F(EnvironmentTest, ResetInitialVisionIsNotBlack) { constexpr int vision_w = 16; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false, - 60.f); + file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false); for (const auto states = env.reset(100.f, 100.f); const auto &[vision, proprioception]: states) { @@ -85,8 +83,7 @@ TEST_F(EnvironmentTest, ResetGoldenImage) { constexpr int vision_w = 16; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false, - 60.f); + file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false); env.seed(42); @@ -151,7 +148,7 @@ TEST_F(EnvironmentTest, ResetProprioceptionNonEmpty) { constexpr float frequency = 1.f / 60.f; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, 8, 8, 1, false, false, 60.f); + file_reader, graphics_backend, nb_tanks, frequency, 8, 8, 1, false, false); for (const auto states = env.reset(100.f, 100.f); const auto &[vision, proprioception]: states) { @@ -170,7 +167,7 @@ TEST_F(EnvironmentTest, ResetCallsOnResetPhysics) { constexpr float frequency = 1.f / 60.f; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, 8, 8, 1, false, false, 60.f); + file_reader, graphics_backend, nb_tanks, frequency, 8, 8, 1, false, false); env.reset(100.f, 100.f); @@ -190,8 +187,7 @@ TEST_F(EnvironmentTest, StepReturnsCorrectNumberOfTuples) { constexpr int vision_w = 16; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false, - 60.f); + file_reader, graphics_backend, nb_tanks, frequency, vision_h, vision_w, 1, false, false); env.reset(100.f, 100.f); @@ -216,7 +212,7 @@ TEST_F(EnvironmentTest, StepRewardAndDoneAreValid) { constexpr float frequency = 1.f / 60.f; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false); env.reset(100.f, 100.f); @@ -243,7 +239,7 @@ TEST_F(EnvironmentTest, StepCallsOnDraw) { constexpr float frequency = 1.f / 60.f; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false); env.reset(100.f, 100.f); @@ -272,7 +268,7 @@ TEST_F(EnvironmentTest, MultipleStepsDoNotCrash) { constexpr float frequency = 1.f / 60.f; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false); env.reset(100.f, 100.f); @@ -298,7 +294,7 @@ TEST_F(EnvironmentTest, ResetCallsOnResetDrawables) { constexpr float frequency = 1.f / 60.f; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false); env.reset(100.f, 100.f); @@ -316,7 +312,7 @@ TEST_F(EnvironmentTest, FullLifecycle) { constexpr float frequency = 1.f / 60.f; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false); // First episode const auto initial_states = env.reset(100.f, 100.f); @@ -346,7 +342,7 @@ TEST_F(EnvironmentTest, StopDrawingDoubleCallDoesNotCrash) { constexpr float frequency = 1.f / 60.f; TestTanksEnvironment env( - file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false, 60.f); + file_reader, graphics_backend, nb_tanks, frequency, 16, 16, 1, false, false); env.reset(100.f, 100.f); diff --git a/arenai_desktop/src/core/game_environment.cpp b/arenai_desktop/src/core/game_environment.cpp index 9a78551f..ca9ed725 100644 --- a/arenai_desktop/src/core/game_environment.cpp +++ b/arenai_desktop/src/core/game_environment.cpp @@ -25,9 +25,8 @@ namespace arenai::desktop { : BaseTanksEnvironment( std::make_shared(asset_folder_path), view::make_vulkan_backend(settings.vision_gpu), settings.nb_tanks, wanted_frequency, - // no starving timeout in the playable game; 180s matches the training - // normalization of the remaining-time proprioception (5400 steps at 30Hz) - vision_height, vision_width, 8, true, false, 180.f), + // no starving timeout in the playable game + vision_height, vision_width, 8, true, false), windowed_backend(graphics_backend), asset_file_reader(std::make_shared(asset_folder_path)), player_tank(std::nullptr_t()), player_renderer(std::nullptr_t()), diff --git a/arenai_model/include/arenai_model/constants.h b/arenai_model/include/arenai_model/constants.h index c87e30c4..fca7ecdf 100644 --- a/arenai_model/include/arenai_model/constants.h +++ b/arenai_model/include/arenai_model/constants.h @@ -11,8 +11,8 @@ namespace arenai::model { constexpr float WHEEL_RADIAL_VELOCITY = std::numbers::pi * 5.f; - // (pos, vel, forward, up, ang_vel) * (9 items: 6 wheels, 1 chassis, 1 turret, 1 canon) - chassis pos + remaining shells ratio + cooldown ratio + timeout ratio - constexpr int ENEMY_PROPRIOCEPTION_SIZE = (3 + 3 + 3 + 3 + 3) * (6 + 3) - 3 + 1 + 1 + 1; + // (pos, vel, forward, up, ang_vel) * (9 items: 6 wheels, 1 chassis, 1 turret, 1 canon) - chassis pos + remaining shells ratio + cooldown ratio + constexpr int ENEMY_PROPRIOCEPTION_SIZE = (3 + 3 + 3 + 3 + 3) * (6 + 3) - 3 + 1 + 1; constexpr int ENEMY_NB_CONTINUOUS_ACTION = 2 + 2; constexpr int ENEMY_NB_DISCRETE_ACTION = 2; diff --git a/arenai_model/include/arenai_model/tank_factory.h b/arenai_model/include/arenai_model/tank_factory.h index 096eb640..2636253f 100644 --- a/arenai_model/include/arenai_model/tank_factory.h +++ b/arenai_model/include/arenai_model/tank_factory.h @@ -22,8 +22,7 @@ namespace arenai::model { virtual std::unique_ptr make_enemy_tank( const std::shared_ptr &file_reader, - const std::string &tank_prefix_name, glm::vec3 chassis_pos, bool apply_timeout, - float max_episode_seconds) = 0; + const std::string &tank_prefix_name, glm::vec3 chassis_pos, bool apply_timeout) = 0; virtual std::unique_ptr make_player_tank( const std::shared_ptr &file_reader, diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 9860e158..54046c7a 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -45,7 +45,7 @@ namespace arenai::model { JoltPhysicEngine &engine, const std::shared_ptr &file_reader, const std::string &tank_prefix_name, const glm::vec3 chassis_pos, - const float wanted_frame_frequency, const bool apply_timeout, float max_episode_seconds) + const float wanted_frame_frequency, const bool apply_timeout) : JoltTank( engine, file_reader, tank_prefix_name, chassis_pos, wanted_frame_frequency, [this](const ShellItem *shell, const ShellContactInfo &info, Item *item) { @@ -69,8 +69,7 @@ namespace arenai::model { remaining_frames(max_frames_without_hit), nb_frames_added_when_hit(static_cast(3.f / wanted_frame_frequency)), nb_frames_added_when_kill(static_cast(15.f / wanted_frame_frequency)), - has_hit(false), has_kill(false), has_fired(false), - max_episode_frames(static_cast(max_episode_seconds / wanted_frame_frequency)) {} + has_hit(false), has_kill(false), has_fired(false) {} float JoltEnemyTank::compute_hit_reward( const glm::vec3 &fire_pos, const glm::vec3 &enemy_pos, const glm::vec3 &shell_pos) const { @@ -370,10 +369,6 @@ namespace arenai::model { result.push_back(static_cast(nb_shells) / static_cast(max_shells)); result.push_back( static_cast(curr_cooldown_frame) / static_cast(fire_cooldown_frames)); - // hit/kill bonuses can push remaining_frames above the base window, hence the clamp - result.push_back(std::clamp( - static_cast(remaining_frames) / static_cast(max_frames_without_hit), 0.f, - 1.f)); return result; } diff --git a/arenai_model/src/tank/jolt_enemy_tank.h b/arenai_model/src/tank/jolt_enemy_tank.h index 3abb9792..b4fdef40 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.h +++ b/arenai_model/src/tank/jolt_enemy_tank.h @@ -41,7 +41,7 @@ namespace arenai::model { JoltPhysicEngine &engine, const std::shared_ptr &file_reader, const std::string &tank_prefix_name, glm::vec3 chassis_pos, - float wanted_frame_frequency, bool apply_timeout, float max_episode_seconds); + float wanted_frame_frequency, bool apply_timeout); float get_reward() const override; @@ -98,7 +98,6 @@ namespace arenai::model { int remaining_frames; int nb_frames_added_when_hit; int nb_frames_added_when_kill; - int max_episode_frames; bool has_hit; bool has_kill; diff --git a/arenai_model/src/tank/jolt_tank_factory.cpp b/arenai_model/src/tank/jolt_tank_factory.cpp index 9aae042a..54363ffe 100644 --- a/arenai_model/src/tank/jolt_tank_factory.cpp +++ b/arenai_model/src/tank/jolt_tank_factory.cpp @@ -17,11 +17,10 @@ namespace arenai::model { std::unique_ptr JoltTankFactory::make_enemy_tank( const std::shared_ptr &file_reader, - const std::string &tank_prefix_name, glm::vec3 chassis_pos, bool apply_timeout, - float max_episode_seconds) { + const std::string &tank_prefix_name, glm::vec3 chassis_pos, bool apply_timeout) { return std::make_unique( engine, file_reader, tank_prefix_name, chassis_pos, wanted_frame_frequency, - apply_timeout, max_episode_seconds); + apply_timeout); } std::unique_ptr JoltTankFactory::make_player_tank( diff --git a/arenai_model/src/tank/jolt_tank_factory.h b/arenai_model/src/tank/jolt_tank_factory.h index acc4b249..5081ad09 100644 --- a/arenai_model/src/tank/jolt_tank_factory.h +++ b/arenai_model/src/tank/jolt_tank_factory.h @@ -17,8 +17,8 @@ namespace arenai::model { std::unique_ptr make_enemy_tank( const std::shared_ptr &file_reader, - const std::string &tank_prefix_name, glm::vec3 chassis_pos, bool apply_timeout, - float max_episode_seconds) override; + const std::string &tank_prefix_name, glm::vec3 chassis_pos, + bool apply_timeout) override; std::unique_ptr make_player_tank( const std::shared_ptr &file_reader, diff --git a/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp b/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp index 8eff76d0..5bbaea12 100644 --- a/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp +++ b/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp @@ -22,8 +22,7 @@ using namespace arenai::controller; TEST_F(EnemyTankTest, DeadWhenSingleWheelDestroyed) { add_ground(); - const auto tank = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + const auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -54,7 +53,7 @@ TEST_F(EnemyTankTest, DeadWhenSingleWheelDestroyed) { TEST_F(EnemyTankTest, OnDeathMultipleCallsDoNotCrash) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -78,7 +77,7 @@ TEST_F(EnemyTankTest, OnDeathMultipleCallsDoNotCrash) { TEST_F(EnemyTankTest, OnDeathBeforeDeathDoesNothing) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -94,10 +93,8 @@ TEST_F(EnemyTankTest, OnDeathBeforeDeathDoesNothing) { TEST_F(EnemyTankTest, RewardWhenAllEnemiesDeadAndShellFired) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -132,7 +129,7 @@ TEST_F(EnemyTankTest, RewardWhenAllEnemiesDeadAndShellFired) { TEST_F(EnemyTankTest, RewardNoNaNWhenAloneInTankList) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -159,7 +156,7 @@ TEST_F(EnemyTankTest, RewardNoNaNWhenAloneInTankList) { TEST_F(EnemyTankTest, ShellHitsGroundNoRewardNoCrash) { add_ground(); // point the tank away from any enemy so the shell hits the ground - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -197,7 +194,7 @@ TEST_F(EnemyTankTest, ShellHitsGroundNoRewardNoCrash) { TEST_F(EnemyTankTest, SuicideDetectionWhenFlipped) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -217,10 +214,8 @@ TEST_F(EnemyTankTest, SuicideDetectionWhenFlipped) { TEST_F(EnemyTankTest, HasHitOtherTankResetsAfterCall) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -255,11 +250,11 @@ TEST_F(EnemyTankTest, ShellReserveRegeneratesOverTime) { constexpr int initial_shells = 10; constexpr float seconds_per_regen = 1.5f; - // proprioception ends with (reserve, cooldown, timeout) ratios - constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 3; + // proprioception ends with (reserve, cooldown) ratios + constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 2; add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); float last_reserve = tank->get_proprioception()[proprioception_reserve_index]; ASSERT_FLOAT_EQ( @@ -314,11 +309,11 @@ TEST_F(EnemyTankTest, ShellReserveRegenerationIsCappedAtMaximalReserve) { constexpr int max_shells = 30; constexpr float seconds_per_regen = 1.5f; - // proprioception ends with (reserve, cooldown, timeout) ratios - constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 3; + // proprioception ends with (reserve, cooldown) ratios + constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 2; add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); for (int i = 0; i < static_cast(nb_frames_one_second * seconds_per_regen * max_shells); i++) { diff --git a/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp b/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp index babe512f..4ecbce7d 100644 --- a/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp +++ b/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp @@ -29,7 +29,7 @@ TEST_F(PlayerTankTest, ScoreZeroAtCreation) { TEST_F(PlayerTankTest, ScoreIncreasesOnHit) { add_ground(); const auto player = tank_factory->make_player_tank(file_reader, "player", {0.f, 5.f, 0.f}); - auto enemy = tank_factory->make_enemy_tank(file_reader, "enemy", {0.f, 5.f, 30.f}, false, 60.f); + auto enemy = tank_factory->make_enemy_tank(file_reader, "enemy", {0.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -47,8 +47,7 @@ TEST_F(PlayerTankTest, ScoreIncreasesOnHit) { TEST_F(PlayerTankTest, ScoreHigherOnKillThanHit) { add_ground(); const auto player = tank_factory->make_player_tank(file_reader, "player", {0.f, 5.f, 0.f}); - const auto enemy = - tank_factory->make_enemy_tank(file_reader, "enemy", {0.f, 5.f, 30.f}, false, 60.f); + const auto enemy = tank_factory->make_enemy_tank(file_reader, "enemy", {0.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); diff --git a/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp b/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp index 143557eb..1ec2ac38 100644 --- a/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp +++ b/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp @@ -18,7 +18,7 @@ using namespace arenai::utils; TEST_F(ProprioceptionTest, ProprioceptionSizeCorrect) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -30,7 +30,7 @@ TEST_F(ProprioceptionTest, ProprioceptionSizeCorrect) { TEST_F(ProprioceptionTest, ProprioceptionNoNaN) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -45,7 +45,7 @@ TEST_F(ProprioceptionTest, ProprioceptionNoNaN) { TEST_F(ProprioceptionTest, ProprioceptionNoInfinity) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); for (int i = 0; i < 10; i++) engine->step(1.f / 60.f); @@ -59,7 +59,7 @@ TEST_F(ProprioceptionTest, ProprioceptionNoInfinity) { TEST_F(ProprioceptionTest, ProprioceptionContainsSubItemRelativePositions) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -89,7 +89,7 @@ TEST_F(ProprioceptionTest, ProprioceptionContainsSubItemRelativePositions) { TEST_F(ProprioceptionTest, ProprioceptionConsistentSize) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -106,7 +106,7 @@ TEST_F(ProprioceptionTest, ProprioceptionConsistentSize) { TEST_F(ProprioceptionTest, ProprioceptionForwardAndUpVectorsValid) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); diff --git a/arenai_model/tests/src/tests_reward/tests_reward.cpp b/arenai_model/tests/src/tests_reward/tests_reward.cpp index 12c0137e..d84326bc 100644 --- a/arenai_model/tests/src/tests_reward/tests_reward.cpp +++ b/arenai_model/tests/src/tests_reward/tests_reward.cpp @@ -21,10 +21,8 @@ using namespace arenai::controller; TEST_F(RewardTest, RewardZeroWhenAliveNoShot) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -40,10 +38,8 @@ TEST_F(RewardTest, RewardZeroWhenAliveNoShot) { TEST_F(RewardTest, RewardNegativeWhenDead) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -70,10 +66,8 @@ TEST_F(RewardTest, RewardNegativeWhenDead) { TEST_F(RewardTest, DeathPenaltyIsMinusOne) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 0.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {20.f, 0.f, 0.f}, false); engine->step(1.f / 60.f); @@ -102,10 +96,8 @@ TEST_F(RewardTest, DeathPenaltyIsMinusOne) { TEST_F(RewardTest, RewardPositiveOnHit) { add_ground(); // spawn tanks high enough so all parts start above ground and settle cleanly - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); // settle on ground (300 frames = 5s at 60fps) for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -142,10 +134,8 @@ TEST_F(RewardTest, RewardPositiveOnHit) { TEST_F(RewardTest, RewardUnderOneAfterHit) { add_ground(); // spawn tanks high enough so all parts start above ground and settle cleanly - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); // settle on ground (300 frames = 5s at 60fps) for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -201,10 +191,8 @@ TEST_F(RewardTest, RewardUnderOneAfterHit) { TEST_F(RewardTest, MissedShellPaysNothing) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {15.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {15.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -241,15 +229,13 @@ TEST_F(RewardTest, MissedShellPaysNothing) { TEST_F(RewardTest, NoRewardWhenShootingAWreck) { - // proprioception ends with (reserve, cooldown, timeout) ratios - constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 3; + // proprioception ends with (reserve, cooldown) ratios + constexpr int proprioception_reserve_index = ENEMY_PROPRIOCEPTION_SIZE - 2; add_ground(); // spawn tanks high enough so all parts start above ground and settle cleanly - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); // settle on ground (300 frames = 5s at 60fps) for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -295,10 +281,8 @@ TEST_F(RewardTest, NoRewardWhenShootingAWreck) { TEST_F(RewardTest, NoKillRewardWhenHittingAnotherPartOfAWreck) { add_ground(); // spawn tanks high enough so all parts start above ground and settle cleanly - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); // settle on ground (300 frames = 5s at 60fps) for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -348,7 +332,7 @@ TEST_F(RewardTest, NoKillRewardWhenHittingAnotherPartOfAWreck) { TEST_F(RewardTest, ZeroRewardWithEmptyTankList) { add_ground(); - auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); diff --git a/arenai_model/tests/src/tests_shell/tests_shell.cpp b/arenai_model/tests/src/tests_shell/tests_shell.cpp index f7cacf10..5d61239f 100644 --- a/arenai_model/tests/src/tests_shell/tests_shell.cpp +++ b/arenai_model/tests/src/tests_shell/tests_shell.cpp @@ -19,8 +19,7 @@ using namespace arenai::controller; TEST_F(ShellTest, FireCreatesShellItem) { add_ground(); - const auto tank = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + const auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -41,8 +40,7 @@ TEST_F(ShellTest, FireCreatesShellItem) { TEST_F(ShellTest, ShellDestroyedAfterLifetime) { add_ground(); - const auto tank = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + const auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -70,10 +68,8 @@ TEST_F(ShellTest, ShellDestroyedAfterLifetime) { TEST_F(ShellTest, ShellHitsEnemyTank) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -93,10 +89,8 @@ TEST_F(ShellTest, ShellHitsEnemyTank) { TEST_F(ShellTest, ShellDestroyedOnContact) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -125,8 +119,7 @@ TEST_F(ShellTest, ShellDestroyedOnContact) { TEST_F(ShellTest, NoFireNoNewItems) { add_ground(); - const auto tank = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); + const auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); engine->step(1.f / 60.f); @@ -147,10 +140,8 @@ TEST_F(ShellTest, NoFireNoNewItems) { TEST_F(ShellTest, ShellContactCallbackSetsReward) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -211,10 +202,8 @@ namespace { TEST_F(ShellTest, ShellImpactDealsExactlyOneDamage) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -237,10 +226,8 @@ TEST_F(ShellTest, ShellImpactDealsExactlyOneDamage) { TEST_F(ShellTest, TankSurvivesUntilAPartsHealthPointsAreSpent) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); @@ -278,10 +265,8 @@ TEST_F(ShellTest, TankSurvivesUntilAPartsHealthPointsAreSpent) { TEST_F(ShellTest, SpentShellDealsNoFurtherDamage) { add_ground(); - auto tank_a = - tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false, 60.f); - auto tank_b = - tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false, 60.f); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}, false); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}, false); for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); From e6f8b5bf3bc7c3cad3df82706cf9bedf5ff89b5b Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Tue, 15 Sep 2026 23:26:37 +0200 Subject: [PATCH 26/57] silu for ltc, 1000m target spawn zone --- arenai_agent/src/main.cpp | 4 ++-- arenai_agent/src/networks/recurrent/liquid_actor.cpp | 2 +- arenai_agent/src/networks/recurrent/liquid_critic.cpp | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index d166cb2d..1a651a21 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -36,8 +36,8 @@ int main(const int argc, char **argv) { parser.add_argument("--vision_width").scan<'i', int>().default_value(256); parser.add_argument("--initial_spawn_width").scan<'g', float>().default_value(250.f); parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(250.f); - parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(2000.f); - parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(2000.f); + parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(1000.f); + parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(1000.f); parser.add_argument("--curriculum_delta").scan<'g', float>().default_value(50.f); parser.add_argument("--curriculum_ratio_low").scan<'g', float>().default_value(0.02f); parser.add_argument("--curriculum_ratio_high").scan<'g', float>().default_value(0.04f); diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.cpp b/arenai_agent/src/networks/recurrent/liquid_actor.cpp index 8df5e05d..1cb18978 100644 --- a/arenai_agent/src/networks/recurrent/liquid_actor.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_actor.cpp @@ -34,7 +34,7 @@ namespace arenai::agent { "liquid", std::make_shared( neuron_number, hidden_size_sensors + vision_encoder->get_output_size(), neuron_number, unfolding_steps, - [](const torch::Tensor &t) { return torch::tanh(t); }, delta_t))), + [](const torch::Tensor &t) { return torch::silu(t); }, delta_t))), mode(register_module( "mode", torch::nn::Sequential( diff --git a/arenai_agent/src/networks/recurrent/liquid_critic.cpp b/arenai_agent/src/networks/recurrent/liquid_critic.cpp index ab373af7..5f9ca239 100644 --- a/arenai_agent/src/networks/recurrent/liquid_critic.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_critic.cpp @@ -30,7 +30,7 @@ namespace arenai::agent { "liquid", std::make_shared( neuron_number, hidden_size_sensors + vision_encoder->get_output_size(), neuron_number, unfolding_steps, - [](const torch::Tensor &t) { return torch::tanh(t); }, delta_t))), + [](const torch::Tensor &t) { return torch::silu(t); }, delta_t))), to_value(register_module("to_value", torch::nn::Linear(neuron_number, 1))) { vision_encoder->apply(init_hidden_weights); From 9ab391e790ebd724224ab8428350cf02268dacfc Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Wed, 16 Sep 2026 20:20:10 +0200 Subject: [PATCH 27/57] fix starving ending : handle it like a truncation (using actual state for next value) + remove penalty at starving as the critic can not predict it --- arenai_agent/src/agents/ppo/ppo_collector.cpp | 6 ++- arenai_agent/src/agents/ppo/ppo_collector.h | 4 +- .../src/agents/ppo/ppo_rollout_buffer.cpp | 4 +- .../src/agents/ppo/ppo_rollout_buffer.h | 4 ++ arenai_agent/src/agents/ppo/ppo_trainer.cpp | 6 ++- .../ppo_liquid/liquid_ppo_collector.cpp | 5 ++- .../agents/ppo_liquid/liquid_ppo_collector.h | 4 +- .../ppo_liquid/liquid_ppo_rollout_buffer.cpp | 2 + .../ppo_liquid/liquid_ppo_rollout_buffer.h | 4 ++ .../agents/ppo_liquid/liquid_ppo_trainer.cpp | 6 ++- arenai_agent/src/agents/sac/sac_collector.cpp | 9 +++- arenai_agent/src/agents/sac/sac_collector.h | 4 +- .../src/agents/sac/sac_replay_buffer.cpp | 4 ++ .../src/agents/sac/sac_replay_buffer.h | 4 ++ arenai_agent/src/agents/sac/sac_trainer.cpp | 42 ++++++++++--------- arenai_agent/src/agents/step_collector.h | 4 +- arenai_agent/src/agents/torch_types.h | 1 + arenai_agent/src/core/train_environment.cpp | 9 ++-- arenai_agent/src/core/train_environment.h | 2 +- .../src/networks_utils/torch_converter.cpp | 11 +++-- .../src/networks_utils/torch_converter.h | 3 +- arenai_agent/src/train.cpp | 7 ++-- .../src/tests_agents/probe_act_sample.cpp | 2 +- .../tests_liquid_ppo_rollout_buffer.cpp | 27 ++++++++++++ .../tests_liquid_ppo_training.cpp | 12 ++++-- .../tests_agents/tests_ppo_rollout_buffer.cpp | 3 +- .../src/tests_agents/tests_ppo_training.cpp | 3 +- .../src/tests_e2e/tests_e2e_agent_input.cpp | 2 +- .../create_random_step.cpp | 3 +- .../tests_replay_buffer_edge.cpp | 1 + .../tests_replay_buffer_sample.cpp | 2 +- arenai_core/include/arenai_core/environment.h | 2 +- arenai_core/include/arenai_core/types.h | 4 ++ arenai_core/src/environment.cpp | 6 +-- arenai_core/tests/src/tests_environment.cpp | 2 +- arenai_desktop/src/game.cpp | 2 +- .../tests/src/tests_e2e_agent_input.cpp | 2 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 18 ++++---- arenai_model/src/tank/jolt_enemy_tank.h | 3 ++ 39 files changed, 171 insertions(+), 68 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_collector.cpp b/arenai_agent/src/agents/ppo/ppo_collector.cpp index b0e30543..8f87e0cc 100644 --- a/arenai_agent/src/agents/ppo/ppo_collector.cpp +++ b/arenai_agent/src/agents/ppo/ppo_collector.cpp @@ -21,14 +21,16 @@ namespace arenai::agent { last_discrete_log_prob = discrete_log_prob; } - void PpoStepCollector::on_transition(const torch::Tensor &rewards, const torch::Tensor &done) { + void PpoStepCollector::on_transition( + const torch::Tensor &rewards, const torch::Tensor &done, const torch::Tensor &truncated) { rollout_buffer->add( {.state = last_state, .action = last_action, .continuous_log_prob = last_continuous_log_prob, .discrete_log_prob = last_discrete_log_prob, .reward = rewards, - .done = done}); + .done = done, + .truncated = truncated}); } void PpoStepCollector::on_episode_end(const TorchState &final_state) { diff --git a/arenai_agent/src/agents/ppo/ppo_collector.h b/arenai_agent/src/agents/ppo/ppo_collector.h index f98cb8a8..89c7a1c6 100644 --- a/arenai_agent/src/agents/ppo/ppo_collector.h +++ b/arenai_agent/src/agents/ppo/ppo_collector.h @@ -22,7 +22,9 @@ namespace arenai::agent { const TorchState &state, const TorchAction &action, const torch::Tensor &continuous_log_prob, const torch::Tensor &discrete_log_prob); - void on_transition(const torch::Tensor &rewards, const torch::Tensor &done) override; + void on_transition( + const torch::Tensor &rewards, const torch::Tensor &done, + const torch::Tensor &truncated) override; void on_episode_end(const TorchState &final_state) override; diff --git a/arenai_agent/src/agents/ppo/ppo_rollout_buffer.cpp b/arenai_agent/src/agents/ppo/ppo_rollout_buffer.cpp index cf382337..1f7bbc05 100644 --- a/arenai_agent/src/agents/ppo/ppo_rollout_buffer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_rollout_buffer.cpp @@ -38,7 +38,8 @@ namespace arenai::agent { .continuous_log_prob = step.continuous_log_prob.detach().cpu(), .discrete_log_prob = step.discrete_log_prob.detach().cpu(), .reward = step.reward.detach().cpu(), - .done = step.done.detach().cpu()}, + .done = step.done.detach().cpu(), + .truncated = step.truncated.detach().cpu()}, .valid = valid}); // the freshly added step is pending: its closing observation is not known yet @@ -88,6 +89,7 @@ namespace arenai::agent { stack([](const StoredStep &s) { return s.step.discrete_log_prob; }), .rewards = stack([](const StoredStep &s) { return s.step.reward; }), .dones = stack([](const StoredStep &s) { return s.step.done; }), + .truncateds = stack([](const StoredStep &s) { return s.step.truncated; }), .bootstrap_state = bootstrap_state, .valids = stack([](const StoredStep &s) { return s.valid; }).unsqueeze(-1)}; diff --git a/arenai_agent/src/agents/ppo/ppo_rollout_buffer.h b/arenai_agent/src/agents/ppo/ppo_rollout_buffer.h index 924a5941..973f8772 100644 --- a/arenai_agent/src/agents/ppo/ppo_rollout_buffer.h +++ b/arenai_agent/src/agents/ppo/ppo_rollout_buffer.h @@ -21,6 +21,8 @@ namespace arenai::agent { torch::Tensor discrete_log_prob; torch::Tensor reward; torch::Tensor done; + // done whose value target must bootstrap instead of cutting the return (famine) + torch::Tensor truncated; }; // On-policy rollout stacked on the time dimension: every tensor is [T, nb_tanks, ...] @@ -31,6 +33,8 @@ namespace arenai::agent { torch::Tensor discrete_log_probs; torch::Tensor rewards; torch::Tensor dones; + // dones whose value target must bootstrap instead of cutting the return + torch::Tensor truncateds; // [nb_tanks, ...] observation closing the last step, for the value bootstrap TorchState bootstrap_state; // [T, nb_tanks, 1] whether the (step, tank) pair is a live transition diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 94bc7b8d..98b4303e 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -276,9 +276,13 @@ namespace arenai::agent { const auto rewards = rollout.rewards.to(torch::kFloat); const auto dones = rollout.dones.to(torch::kFloat); + const auto truncateds = rollout.truncateds.to(torch::kFloat); const auto valids = rollout.valids.to(torch::kFloat); - const auto deltas = rewards + gamma * next_values * (1.f - dones) - values; + // a truncated step bootstraps on its own value (the last alive observation): + // the post-mortem next state is not a state the counterfactual life would reach + const auto deltas = + rewards + gamma * (next_values * (1.f - dones) + values * truncateds) - values; auto advantages = torch::zeros_like(deltas); auto gae = torch::zeros({nb_tanks, 1}, deltas.options()); diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.cpp index 8fc8eefd..d0931a89 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.cpp +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.cpp @@ -25,8 +25,8 @@ namespace arenai::agent { last_actor_hidden = actor_hidden; } - void - LiquidPpoStepCollector::on_transition(const torch::Tensor &rewards, const torch::Tensor &done) { + void LiquidPpoStepCollector::on_transition( + const torch::Tensor &rewards, const torch::Tensor &done, const torch::Tensor &truncated) { rollout_buffer->add( {.state = last_state, .action = last_action, @@ -34,6 +34,7 @@ namespace arenai::agent { .discrete_log_prob = last_discrete_log_prob, .reward = rewards, .done = done, + .truncated = truncated, .actor_hidden = last_actor_hidden, .episode_start = next_episode_start}); diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.h b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.h index 155735db..65c42e5b 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.h +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_collector.h @@ -26,7 +26,9 @@ namespace arenai::agent { const torch::Tensor &continuous_log_prob, const torch::Tensor &discrete_log_prob, const torch::Tensor &actor_hidden); - void on_transition(const torch::Tensor &rewards, const torch::Tensor &done) override; + void on_transition( + const torch::Tensor &rewards, const torch::Tensor &done, + const torch::Tensor &truncated) override; void on_episode_end(const TorchState &final_state) override; diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.cpp index 376a0efc..300d3083 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.cpp +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.cpp @@ -39,6 +39,7 @@ namespace arenai::agent { .discrete_log_prob = step.discrete_log_prob.detach().cpu(), .reward = step.reward.detach().cpu(), .done = step.done.detach().cpu(), + .truncated = step.truncated.detach().cpu(), .actor_hidden = step.actor_hidden.detach().cpu(), .episode_start = step.episode_start}, .valid = valid}); @@ -94,6 +95,7 @@ namespace arenai::agent { stack([](const StoredStep &s) { return s.step.discrete_log_prob; }), .rewards = stack([](const StoredStep &s) { return s.step.reward; }), .dones = stack([](const StoredStep &s) { return s.step.done; }), + .truncateds = stack([](const StoredStep &s) { return s.step.truncated; }), .bootstrap_state = bootstrap_state, .valids = stack([](const StoredStep &s) { return s.valid; }).unsqueeze(-1), .actor_hiddens = stack([](const StoredStep &s) { return s.step.actor_hidden; }), diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.h b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.h index 8d5117d6..9f3bb8ca 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.h +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_rollout_buffer.h @@ -21,6 +21,8 @@ namespace arenai::agent { torch::Tensor discrete_log_prob; torch::Tensor reward; torch::Tensor done; + // done whose value target must bootstrap instead of cutting the return (famine) + torch::Tensor truncated; // [nb_tanks, neuron_number] actor liquid state opening the step torch::Tensor actor_hidden; // the step opens a fresh episode (the liquid states were re-drawn) @@ -35,6 +37,8 @@ namespace arenai::agent { torch::Tensor discrete_log_probs; torch::Tensor rewards; torch::Tensor dones; + // dones whose value target must bootstrap instead of cutting the return + torch::Tensor truncateds; // [nb_tanks, ...] observation closing the last step, for the value bootstrap TorchState bootstrap_state; // [T, nb_tanks, 1] whether the (step, tank) pair is a live transition diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp index 2395ca17..2dd1165c 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp @@ -355,9 +355,13 @@ namespace arenai::agent { const auto rewards = rollout.rewards.to(torch::kFloat); const auto dones = rollout.dones.to(torch::kFloat); + const auto truncateds = rollout.truncateds.to(torch::kFloat); const auto valids = rollout.valids.to(torch::kFloat); - const auto deltas = rewards + gamma * next_values * (1.f - dones) - values; + // a truncated step bootstraps on its own value (the last alive observation): + // the post-mortem next state is not a state the counterfactual life would reach + const auto deltas = + rewards + gamma * (next_values * (1.f - dones) + values * truncateds) - values; auto advantages = torch::zeros_like(deltas); auto gae = torch::zeros({nb_tanks, 1}, deltas.options()); diff --git a/arenai_agent/src/agents/sac/sac_collector.cpp b/arenai_agent/src/agents/sac/sac_collector.cpp index e8ef7c12..c97c0e82 100644 --- a/arenai_agent/src/agents/sac/sac_collector.cpp +++ b/arenai_agent/src/agents/sac/sac_collector.cpp @@ -17,9 +17,14 @@ namespace arenai::agent { last_action = action; } - void SacStepCollector::on_transition(const torch::Tensor &rewards, const torch::Tensor &done) { + void SacStepCollector::on_transition( + const torch::Tensor &rewards, const torch::Tensor &done, const torch::Tensor &truncated) { replay_buffer->add( - {.state = last_state, .action = last_action, .reward = rewards, .done = done}); + {.state = last_state, + .action = last_action, + .reward = rewards, + .done = done, + .truncated = truncated}); } void SacStepCollector::on_episode_end(const TorchState &final_state) { diff --git a/arenai_agent/src/agents/sac/sac_collector.h b/arenai_agent/src/agents/sac/sac_collector.h index c93bd6e1..209b9932 100644 --- a/arenai_agent/src/agents/sac/sac_collector.h +++ b/arenai_agent/src/agents/sac/sac_collector.h @@ -20,7 +20,9 @@ namespace arenai::agent { // concrete act-time channel, called by TrainableSacAgent::act void on_act(const TorchState &state, const TorchAction &action); - void on_transition(const torch::Tensor &rewards, const torch::Tensor &done) override; + void on_transition( + const torch::Tensor &rewards, const torch::Tensor &done, + const torch::Tensor &truncated) override; void on_episode_end(const TorchState &final_state) override; diff --git a/arenai_agent/src/agents/sac/sac_replay_buffer.cpp b/arenai_agent/src/agents/sac/sac_replay_buffer.cpp index 0a4b0942..2d8e971c 100644 --- a/arenai_agent/src/agents/sac/sac_replay_buffer.cpp +++ b/arenai_agent/src/agents/sac/sac_replay_buffer.cpp @@ -38,6 +38,7 @@ namespace arenai::agent { store_disc_action_ = make_storage(first_step.action.discrete_action); store_reward_ = make_storage(first_step.reward); store_done_ = make_storage(first_step.done); + store_truncated_ = make_storage(first_step.truncated); const auto bool_cpu = torch::TensorOptions().dtype(torch::kBool).device(cpu); store_sampleable_ = torch::zeros({mem, nb_tanks_}, bool_cpu); @@ -59,6 +60,7 @@ namespace arenai::agent { store_disc_action_[idx].copy_(step.action.discrete_action.detach()); store_reward_[idx].copy_(step.reward); store_done_[idx].copy_(done_bool); + store_truncated_[idx].copy_(step.truncated.to(torch::kBool)); // tanks already terminated before this step have no valid transition to store store_sampleable_[idx].copy_(already_terminated_.logical_not()); @@ -80,6 +82,7 @@ namespace arenai::agent { store_disc_action_[idx].zero_(); store_reward_[idx].zero_(); store_done_[idx].zero_(); + store_truncated_[idx].zero_(); store_sampleable_[idx].fill_(false); already_terminated_.fill_(false); @@ -124,6 +127,7 @@ namespace arenai::agent { .discrete_action = take(store_disc_action_, step_idx)}, .reward = take(store_reward_, step_idx), .done = take(store_done_, step_idx), + .truncated = take(store_truncated_, step_idx), .next_state = { .vision = take(store_vision_, next_idx), .proprioception = take(store_proprioception_, next_idx)}}; diff --git a/arenai_agent/src/agents/sac/sac_replay_buffer.h b/arenai_agent/src/agents/sac/sac_replay_buffer.h index 9f86e96c..4336c331 100644 --- a/arenai_agent/src/agents/sac/sac_replay_buffer.h +++ b/arenai_agent/src/agents/sac/sac_replay_buffer.h @@ -16,6 +16,8 @@ namespace arenai::agent { TorchAction action; torch::Tensor reward; torch::Tensor done; + // done whose value target must bootstrap instead of cutting the return (famine) + torch::Tensor truncated; }; struct SacTrainStep { @@ -23,6 +25,7 @@ namespace arenai::agent { TorchAction action; torch::Tensor reward; torch::Tensor done; + torch::Tensor truncated; TorchState next_state; }; @@ -59,6 +62,7 @@ namespace arenai::agent { torch::Tensor store_disc_action_; torch::Tensor store_reward_; torch::Tensor store_done_; + torch::Tensor store_truncated_; // [mem, nb_tanks] whether the (step, tank) pair can start a sampled transition torch::Tensor store_sampleable_; diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index 4a4d8d98..19592a20 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -100,37 +100,41 @@ namespace arenai::agent { set_train(true); for (int e = 0; e < epochs; e++) { - const auto [state, action, reward, done, next_state] = + const auto [state, action, reward, done, truncated, next_state] = replay_buffer->sample(batch_size, actor->parameters().back().device()); torch::Tensor target_q_values; { torch::NoGradGuard no_grad; - const auto [next_mu, next_sigma, next_discrete_proba] = - actor->act(next_state.vision, next_state.proprioception); + const auto soft_value = [&](const TorchState &s) { + const auto [mu, sigma, discrete_proba] = actor->act(s.vision, s.proprioception); - const auto next_continuous_action = truncated_normal_sample(next_mu, next_sigma); - const auto next_continuous_entropy = truncated_normal_entropy(next_mu, next_sigma); + const auto continuous_action = truncated_normal_sample(mu, sigma); + const auto continuous_entropy = truncated_normal_entropy(mu, sigma); - const auto next_discrete_entropy = multinomial_entropy(next_discrete_proba); + const auto discrete_entropy = multinomial_entropy(discrete_proba); - const auto next_target_q_values_1 = target_critic_1->value_per_discrete_action( - next_state.vision, next_state.proprioception, next_continuous_action); - const auto next_target_q_values_2 = target_critic_2->value_per_discrete_action( - next_state.vision, next_state.proprioception, next_continuous_action); + const auto target_q_values_1 = target_critic_1->value_per_discrete_action( + s.vision, s.proprioception, continuous_action); + const auto target_q_values_2 = target_critic_2->value_per_discrete_action( + s.vision, s.proprioception, continuous_action); - const auto next_min_q_value = torch::sum( - next_discrete_proba - * torch::min(next_target_q_values_1, next_target_q_values_2), - -1, true); + const auto min_q_value = torch::sum( + discrete_proba * torch::min(target_q_values_1, target_q_values_2), -1, + true); - const auto target_v_value = - next_min_q_value - + torch::sum(alpha_continuous->alpha() * next_continuous_entropy, -1, true) - + torch::sum(alpha_discrete->alpha() * next_discrete_entropy, -1, true); + return min_q_value + + torch::sum(alpha_continuous->alpha() * continuous_entropy, -1, true) + + torch::sum(alpha_discrete->alpha() * discrete_entropy, -1, true); + }; - target_q_values = reward + (1.f - done.to(torch::kFloat)) * gamma * target_v_value; + // a truncated step bootstraps on its own state (the last alive observation): + // the post-mortem next state is not a state the counterfactual life would reach + target_q_values = reward + + gamma + * ((1.f - done.to(torch::kFloat)) * soft_value(next_state) + + truncated.to(torch::kFloat) * soft_value(state)); } // critic 1 diff --git a/arenai_agent/src/agents/step_collector.h b/arenai_agent/src/agents/step_collector.h index 9ab8975e..74a1a2d5 100644 --- a/arenai_agent/src/agents/step_collector.h +++ b/arenai_agent/src/agents/step_collector.h @@ -16,7 +16,9 @@ namespace arenai::agent { public: virtual ~AbstractStepCollector() = default; - virtual void on_transition(const torch::Tensor &rewards, const torch::Tensor &done) = 0; + virtual void on_transition( + const torch::Tensor &rewards, const torch::Tensor &done, + const torch::Tensor &truncated) = 0; virtual void on_episode_end(const TorchState &final_state) = 0; }; diff --git a/arenai_agent/src/agents/torch_types.h b/arenai_agent/src/agents/torch_types.h index 7b6dab83..d18b902e 100644 --- a/arenai_agent/src/agents/torch_types.h +++ b/arenai_agent/src/agents/torch_types.h @@ -22,6 +22,7 @@ namespace arenai::agent { TorchState states; torch::Tensor rewards; torch::Tensor is_done; + torch::Tensor is_truncated; }; }// namespace arenai::agent diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index 4510fe91..af4bbf1b 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -46,7 +46,7 @@ namespace arenai::agent { kill_metric(std::make_shared("kill", 16, 1)), nb_kills_episode(0), nb_fires_episode(0), nb_hits_episode(0) {} - std::vector> + std::vector> TrainTankEnvironment::step(const float time_delta, const std::vector &actions) { // tanks flagged done on a previous step already emitted their terminal transition: @@ -125,7 +125,7 @@ namespace arenai::agent { if (has_kill[i]) { nb_kills_per_tanks[i] += 1; } // detect death (kill, suicide or timeout) - if (const auto &[state, reward, is_done] = step_result[i]; is_done) { + if (const auto &[state, reward, is_done, is_truncated] = step_result[i]; is_done) { if (!already_done[i] && !is_suicide[i] && !is_timeout[i]) nb_kills_episode++; done[i] = true; } @@ -139,10 +139,11 @@ namespace arenai::agent { if (tanks_not_done_indexes.size() == 1) { const auto winner_index = tanks_not_done_indexes[0]; - const auto &[state, reward, is_done] = step_result[winner_index]; + const auto &[state, reward, is_done, is_truncated] = step_result[winner_index]; const float win_reward = nb_kills_per_tanks[winner_index] > 0 ? 2.f : 0.f; - step_result[winner_index] = {state, reward + win_reward, true}; + // winning is a genuine termination, never a truncation + step_result[winner_index] = {state, reward + win_reward, true, false}; done[winner_index] = true; } diff --git a/arenai_agent/src/core/train_environment.h b/arenai_agent/src/core/train_environment.h index f8779fcb..02478f6f 100644 --- a/arenai_agent/src/core/train_environment.h +++ b/arenai_agent/src/core/train_environment.h @@ -18,7 +18,7 @@ namespace arenai::agent { const std::filesystem::path &android_assets_path, float wanted_frequency, int max_episode_steps, int vision_height, int vision_width, int vision_num_threads); - std::vector> + std::vector> step(float time_delta, const std::vector &actions) override; std::vector> get_metrics() const; diff --git a/arenai_agent/src/networks_utils/torch_converter.cpp b/arenai_agent/src/networks_utils/torch_converter.cpp index afb05379..4397b457 100644 --- a/arenai_agent/src/networks_utils/torch_converter.cpp +++ b/arenai_agent/src/networks_utils/torch_converter.cpp @@ -81,23 +81,28 @@ namespace arenai::agent { } TorchStep steps_to_tensor( - const std::vector> &steps, + const std::vector> + &steps, const int vision_height, const int vision_width) { std::vector states; std::vector rewards; std::vector are_done; + std::vector are_truncated; - for (const auto &[state, reward, is_done]: steps) { + for (const auto &[state, reward, is_done, is_truncated]: steps) { states.push_back(state); rewards.push_back(torch::tensor({reward}, torch::TensorOptions().dtype(torch::kFloat))); are_done.push_back( torch::tensor({is_done}, torch::TensorOptions().dtype(torch::kBool))); + are_truncated.push_back( + torch::tensor({is_truncated}, torch::TensorOptions().dtype(torch::kBool))); } return { .states = states_to_tensor(states, vision_height, vision_width), .rewards = torch::stack(rewards), - .is_done = torch::stack(are_done)}; + .is_done = torch::stack(are_done), + .is_truncated = torch::stack(are_truncated)}; } }// namespace arenai::agent diff --git a/arenai_agent/src/networks_utils/torch_converter.h b/arenai_agent/src/networks_utils/torch_converter.h index 3063e1dd..53c93b03 100644 --- a/arenai_agent/src/networks_utils/torch_converter.h +++ b/arenai_agent/src/networks_utils/torch_converter.h @@ -23,7 +23,8 @@ namespace arenai::agent { TorchState state_to_tensor(const core::State &state, int vision_height, int vision_width); TorchStep steps_to_tensor( - const std::vector> &steps, + const std::vector> + &steps, int vision_height, int vision_width); }// namespace arenai::agent diff --git a/arenai_agent/src/train.cpp b/arenai_agent/src/train.cpp index ae364304..b23c0f55 100644 --- a/arenai_agent/src/train.cpp +++ b/arenai_agent/src/train.cpp @@ -200,11 +200,12 @@ namespace arenai::agent { // step environment const auto steps = env->step(environment_options.wanted_frequency, actions_for_env); - const auto [torch_next_states, torch_rewards, torch_are_done] = steps_to_tensor( - steps, environment_options.vision_height, environment_options.vision_width); + const auto [torch_next_states, torch_rewards, torch_are_done, torch_are_truncated] = + steps_to_tensor( + steps, environment_options.vision_height, environment_options.vision_width); // complete the pending transition - maybe train - collector->on_transition(torch_rewards, torch_are_done); + collector->on_transition(torch_rewards, torch_are_done, torch_are_truncated); trainer->step(); // step ending stuff diff --git a/arenai_agent/tests/src/tests_agents/probe_act_sample.cpp b/arenai_agent/tests/src/tests_agents/probe_act_sample.cpp index 4d172dac..b3de4585 100644 --- a/arenai_agent/tests/src/tests_agents/probe_act_sample.cpp +++ b/arenai_agent/tests/src/tests_agents/probe_act_sample.cpp @@ -67,7 +67,7 @@ TEST(ProbeActSample, PpoLogProbs) { const auto state = probe_state(3, h, w, nb_sensors); const auto [continuous_action, discrete_action] = agent->act(state, true); - collector->on_transition(torch::randn({3, 1}), torch::zeros({3, 1})); + collector->on_transition(torch::randn({3, 1}), torch::zeros({3, 1}), torch::zeros({3, 1})); collector->on_episode_end(state); torch::NoGradGuard guard; diff --git a/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_rollout_buffer.cpp b/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_rollout_buffer.cpp index 2cebe7e5..54364a26 100644 --- a/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_rollout_buffer.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_rollout_buffer.cpp @@ -32,6 +32,7 @@ LiquidPpoInputStep LiquidPpoRolloutBufferTest::make_step( .discrete_log_prob = torch::randn({NB_TANKS, 1}), .reward = torch::randn({NB_TANKS, 1}), .done = done, + .truncated = torch::zeros({NB_TANKS, 1}), .actor_hidden = torch::randn({NB_TANKS, NEURON_NUMBER}), .episode_start = episode_start}; } @@ -220,6 +221,32 @@ TEST_F(LiquidPpoRolloutBufferTest, TerminatedTankInvalidatesFollowingSteps) { ASSERT_TRUE(valids[2][1].item()); } +TEST_F(LiquidPpoRolloutBufferTest, TruncatedStepIsStackedAndStaysValid) { + LiquidPpoRolloutBuffer buffer; + + // tank 0 starves at the first step: done and truncated together + const auto done = torch::cat({torch::ones({1, 1}), torch::zeros({1, 1})}, 0); + + auto step_0 = make_step(make_state(), done, false); + step_0.truncated = done.clone(); + + buffer.add(step_0); + buffer.add(make_step(make_state())); + buffer.finish_episode(make_state()); + + const auto rollout = buffer.get_rollout(); + const auto truncateds = rollout.truncateds.squeeze(-1); + const auto valids = rollout.valids.squeeze(-1); + + ASSERT_TRUE(truncateds[0][0].item()); + ASSERT_FALSE(truncateds[0][1].item()); + ASSERT_FALSE(truncateds[1][0].item()); + + // the truncated (dying) transition itself is valid, the following one is not + ASSERT_TRUE(valids[0][0].item()); + ASSERT_FALSE(valids[1][0].item()); +} + TEST_F(LiquidPpoRolloutBufferTest, FinishEpisodeResetsTermination) { LiquidPpoRolloutBuffer buffer; diff --git a/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_training.cpp b/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_training.cpp index a97bc5ec..5c23ac4a 100644 --- a/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_training.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_liquid_ppo_training.cpp @@ -99,14 +99,17 @@ TEST_F(LiquidPpoTrainingTest, HiddenStateAdvancesAndResetsAcrossEpisodes) { // first episode: two steps agent->act(make_state(cfg, nb_tanks), true); - collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); + collector->on_transition( + torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); agent->act(make_state(cfg, nb_tanks), true); - collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); + collector->on_transition( + torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); collector->on_episode_end(make_state(cfg, nb_tanks)); // second episode: one step agent->act(make_state(cfg, nb_tanks), true); - collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); + collector->on_transition( + torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); collector->on_episode_end(make_state(cfg, nb_tanks)); const auto rollout = rollout_buffer->get_rollout(); @@ -158,7 +161,8 @@ TEST_F(LiquidPpoTrainingTest, TrainingUpdatesActorParameters) { constexpr int nb_tanks = 2; agent->act(make_state(cfg, nb_tanks), true); - collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); + collector->on_transition( + torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); trainer->step(); } diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp index e4029a07..dacb4434 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp @@ -30,7 +30,8 @@ PpoInputStep PpoRolloutBufferTest::make_step(const TorchState &state, const torc .continuous_log_prob = torch::randn({NB_TANKS, 1}), .discrete_log_prob = torch::randn({NB_TANKS, 1}), .reward = torch::randn({NB_TANKS, 1}), - .done = done}; + .done = done, + .truncated = torch::zeros({NB_TANKS, 1})}; } PpoInputStep PpoRolloutBufferTest::make_step(const TorchState &state) { diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp index 59103955..a7bb7c98 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp @@ -106,7 +106,8 @@ TEST_F(PpoTrainingTest, TrainingUpdatesActorParameters) { constexpr int nb_tanks = 2; agent->act(make_state(cfg, nb_tanks), true); - collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); + collector->on_transition( + torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); trainer->step(); } diff --git a/arenai_agent/tests/src/tests_e2e/tests_e2e_agent_input.cpp b/arenai_agent/tests/src/tests_e2e/tests_e2e_agent_input.cpp index 4549d0be..a55be57c 100644 --- a/arenai_agent/tests/src/tests_e2e/tests_e2e_agent_input.cpp +++ b/arenai_agent/tests/src/tests_e2e/tests_e2e_agent_input.cpp @@ -51,7 +51,7 @@ namespace { const auto steps = env.step(FREQUENCY, actions); states.clear(); - for (const auto &[state, reward, done]: steps) states.push_back(state); + for (const auto &[state, reward, done, truncated]: steps) states.push_back(state); actions = agent.act(states, VISION_HEIGHT, VISION_WIDTH); } diff --git a/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp b/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp index dd7ceb33..d99f29e2 100644 --- a/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp +++ b/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp @@ -23,5 +23,6 @@ SacInputStep create_random_step( {.continuous_action = torch::rand({1, nb_cont_actions}) * 2.f - 1.f, .discrete_action = torch::softmax(torch::randn({1, nb_discrete_actions}), -1)}, .reward = torch::randn({1, 1}), - .done = torch::full({1, 1}, done, torch::kBool)}; + .done = torch::full({1, 1}, done, torch::kBool), + .truncated = torch::zeros({1, 1}, torch::kBool)}; } diff --git a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp index 72df747d..cea5e20c 100644 --- a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp +++ b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp @@ -60,6 +60,7 @@ namespace { step.action.discrete_action[0][0] = 1.0f; step.reward = torch::full({1, 1}, reward); step.done = torch::zeros({1, 1}); + step.truncated = torch::zeros({1, 1}); return step; } }// namespace diff --git a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_sample.cpp b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_sample.cpp index 21db16ec..b2da21fa 100644 --- a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_sample.cpp +++ b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_sample.cpp @@ -17,7 +17,7 @@ namespace { constexpr int SENSORS_NB = 5; void assert_sample_shapes(const SacTrainStep &output, const int expected_batch_size) { - const auto &[state, action, reward, done, next_state] = output; + const auto &[state, action, reward, done, truncated, next_state] = output; ASSERT_EQ(state.vision.ndimension(), 4); ASSERT_EQ(state.vision.size(0), expected_batch_size); diff --git a/arenai_core/include/arenai_core/environment.h b/arenai_core/include/arenai_core/environment.h index bbc3464f..9f5fe840 100644 --- a/arenai_core/include/arenai_core/environment.h +++ b/arenai_core/include/arenai_core/environment.h @@ -29,7 +29,7 @@ namespace arenai::core { float wanted_frequency, int vision_height, int vision_width, int vision_num_threads, bool vision_thread_sleep, bool apply_timeout); - virtual std::vector> + virtual std::vector> step(float time_delta, const std::vector &actions); std::vector reset(float spawn_width, float spawn_height); diff --git a/arenai_core/include/arenai_core/types.h b/arenai_core/include/arenai_core/types.h index 164057e3..9e73f225 100644 --- a/arenai_core/include/arenai_core/types.h +++ b/arenai_core/include/arenai_core/types.h @@ -20,6 +20,10 @@ namespace arenai::core { typedef bool IsDone; + // done from starving out: the episode ends for the tank, but the value target must + // bootstrap instead of cutting the return (truncation, not termination) + typedef bool IsTruncated; + typedef controller::user_input Action; }// namespace arenai::core diff --git a/arenai_core/src/environment.cpp b/arenai_core/src/environment.cpp index 1bd20ad4..6c35af7f 100644 --- a/arenai_core/src/environment.cpp +++ b/arenai_core/src/environment.cpp @@ -30,7 +30,7 @@ namespace arenai::core { apply_timeout(apply_timeout), graphics_backend(graphics_backend), gl_context(graphics_backend->render_context()), rng(dev()), file_reader(file_reader) {} - std::vector> + std::vector> BaseTanksEnvironment::step(const float time_delta, const std::vector &actions) { // 1. apply action @@ -51,7 +51,7 @@ namespace arenai::core { vision_pool_->loop_wait(); // 4. build State - std::vector> result; + std::vector> result; result.reserve(tanks.size()); for (int i = 0; i < tanks.size(); i++) { @@ -61,7 +61,7 @@ namespace arenai::core { // compute/get state result.emplace_back( State(vision_pool_->read_vision(i), tanks[i]->get_proprioception()), - tanks[i]->get_reward(), tanks[i]->is_dead()); + tanks[i]->get_reward(), tanks[i]->is_dead(), tanks[i]->is_timeout()); } return result; diff --git a/arenai_core/tests/src/tests_environment.cpp b/arenai_core/tests/src/tests_environment.cpp index 82c95acc..b2b2da4c 100644 --- a/arenai_core/tests/src/tests_environment.cpp +++ b/arenai_core/tests/src/tests_environment.cpp @@ -222,7 +222,7 @@ TEST_F(EnvironmentTest, StepRewardAndDoneAreValid) { .fire_button = {false}}); for (const auto results = env.step(frequency, actions); - const auto &[state, reward, is_done]: results) { + const auto &[state, reward, is_done, is_truncated]: results) { ASSERT_FALSE(std::isnan(reward)) << "reward should not be NaN"; ASSERT_FALSE(std::isinf(reward)) << "reward should not be Inf"; } diff --git a/arenai_desktop/src/game.cpp b/arenai_desktop/src/game.cpp index 22bed8ef..d3c6caa8 100644 --- a/arenai_desktop/src/game.cpp +++ b/arenai_desktop/src/game.cpp @@ -167,7 +167,7 @@ namespace arenai::desktop { states.clear(); - for (const auto &[state, reward, done]: steps) states.push_back(state); + for (const auto &[state, reward, done, truncated]: steps) states.push_back(state); auto now = std::chrono::steady_clock::now(); auto dt = now - last_time; diff --git a/arenai_desktop/tests/src/tests_e2e_agent_input.cpp b/arenai_desktop/tests/src/tests_e2e_agent_input.cpp index 19857f9f..fad2c04b 100644 --- a/arenai_desktop/tests/src/tests_e2e_agent_input.cpp +++ b/arenai_desktop/tests/src/tests_e2e_agent_input.cpp @@ -54,7 +54,7 @@ namespace { const auto steps = env.step(FREQUENCY, actions); states.clear(); - for (const auto &[state, reward, done]: steps) states.push_back(state); + for (const auto &[state, reward, done, truncated]: steps) states.push_back(state); actions = agent.act(states, VISION_HEIGHT, VISION_WIDTH); } diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 54046c7a..3e026d18 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -65,7 +65,7 @@ namespace arenai::model { curr_cooldown_frame(fire_cooldown_frames), shells_recharged_per_hit(5), nb_frames_per_shell_regen(static_cast(1.5f / wanted_frame_frequency)), curr_frame_shell_regen(0), is_dead_already_triggered(false), apply_timeout(apply_timeout), - max_frames_without_hit(static_cast(30.f / wanted_frame_frequency)), + starved(false), max_frames_without_hit(static_cast(30.f / wanted_frame_frequency)), remaining_frames(max_frames_without_hit), nb_frames_added_when_hit(static_cast(3.f / wanted_frame_frequency)), nb_frames_added_when_kill(static_cast(15.f / wanted_frame_frequency)), @@ -136,9 +136,9 @@ namespace arenai::model { float JoltEnemyTank::get_reward() const { RewardDetail detail; - // 1. death penalty — starving out is the worst outcome: dying by hiding must cost - // more than dying in a fight (which already accumulates received-hit penalties) - detail.death = is_dead() ? (is_timeout() ? -2.f : -1.f) : 0.f; + // 1. death penalty — starving out is a pure truncation: any penalty there is an + // unpredictable shock for the critic (the timer is not observable) + detail.death = is_dead() && !is_timeout() ? -1.f : 0.f; // 2. fired shells reward for (int i = static_cast(tracked_shells.size()) - 1; i >= 0; i--) { @@ -224,8 +224,12 @@ namespace arenai::model { if (has_hit) remaining_frames += nb_frames_added_when_hit; if (has_kill) remaining_frames += nb_frames_added_when_kill; - // starving out (no hit for too long) is a real death: penalized and terminal - if (apply_timeout && remaining_frames <= 0) kill_life_items(); + // starving out (no hit for too long) ends the tank: reported as a truncation to + // the learner — only when the timer is what killed it + if (apply_timeout && remaining_frames <= 0) { + if (!is_dead()) starved = true; + kill_life_items(); + } } void JoltEnemyTank::on_shell_fired(const std::shared_ptr &shell) { @@ -316,7 +320,7 @@ namespace arenai::model { return curr_frame_upside_down > max_frames_upside_down; } - bool JoltEnemyTank::is_timeout() const { return apply_timeout && remaining_frames <= 0; } + bool JoltEnemyTank::is_timeout() const { return starved; } void JoltEnemyTank::on_death() { if (is_dead() && !is_dead_already_triggered) { diff --git a/arenai_model/src/tank/jolt_enemy_tank.h b/arenai_model/src/tank/jolt_enemy_tank.h index b4fdef40..39d9674a 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.h +++ b/arenai_model/src/tank/jolt_enemy_tank.h @@ -94,6 +94,9 @@ namespace arenai::model { bool is_dead_already_triggered; bool apply_timeout; + // latched when the timer actually kills the tank: the timer keeps running after a + // combat death, so the live "remaining_frames <= 0" test would mislabel it later + bool starved; int max_frames_without_hit; int remaining_frames; int nb_frames_added_when_hit; From db5f02a11ff66c1f616347f53d9d2df10dba6259 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Thu, 17 Sep 2026 00:34:34 +0200 Subject: [PATCH 28/57] beta law for vanilla actor --- arenai_agent/src/agents/ppo/ppo_agent.cpp | 10 +++---- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 2 +- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 8 +++--- arenai_agent/src/agents/sac/sac_agent.cpp | 8 +++--- arenai_agent/src/agents/sac/sac_trainer.cpp | 15 ++++++----- arenai_agent/src/networks/actor.cpp | 27 ++++++++++--------- arenai_agent/src/networks/actor.h | 10 ++++--- .../src/tests_agents/probe_act_sample.cpp | 25 +++++++++-------- .../tests/src/tests_networks/tests_actor.cpp | 25 +++++++++-------- .../tests_q_function_consistency.cpp | 8 +++--- 10 files changed, 75 insertions(+), 63 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_agent.cpp b/arenai_agent/src/agents/ppo/ppo_agent.cpp index 439bbb61..63d4237d 100644 --- a/arenai_agent/src/agents/ppo/ppo_agent.cpp +++ b/arenai_agent/src/agents/ppo/ppo_agent.cpp @@ -4,8 +4,8 @@ #include "./ppo_agent.h" +#include "../../distributions/beta_law.h" #include "../../distributions/multinomial.h" -#include "../../distributions/truncated_normal.h" #include "../../networks/constants.h" #include "../../networks_utils/torch_converter.h" #include "../../networks_utils/torch_loader.h" @@ -42,19 +42,19 @@ namespace arenai::agent { torch::NoGradGuard guard; const auto &[vision, sensors] = state; - const auto &[mu, sigma, discrete_proba] = actor->act(vision, sensors); + const auto &[mode, concentration, discrete_proba] = actor->act(vision, sensors); if (sample) { - action.continuous_action = truncated_normal_sample(mu, sigma); + action.continuous_action = beta_law_sample(mode, concentration); action.discrete_action = multinomial_sample(discrete_proba); } else { - action.continuous_action = truncated_normal_mean(mu, sigma); + action.continuous_action = beta_law_mode_action(mode); action.discrete_action = multinomial_max_action(discrete_proba); } // old log-probabilities, kept for the PPO importance ratio continuous_log_prob = - truncated_normal_log_pdf(action.continuous_action, mu, sigma).sum(-1, true); + beta_law_log_proba(action.continuous_action, mode, concentration).sum(-1, true); const auto clamped_proba = torch::clamp(discrete_proba, EPSILON, 1.0 - EPSILON); discrete_log_prob = (action.discrete_action * torch::log(clamped_proba)).sum(-1, true); diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index bad6b3bc..aa5903cd 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -30,7 +30,7 @@ namespace arenai::agent { float clip_epsilon = 0.2f; float target_kl = 0.05f; float grad_norm_max = 0.5f; - float continuous_target_entropy = 0.4f; + float continuous_target_entropy = -0.88f; float discrete_target_entropy_factor = 0.2f; int epochs = 2; int rollout_size = 30 * 30; diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 98b4303e..3dbcc278 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -7,8 +7,8 @@ #include #include +#include "../../distributions/beta_law.h" #include "../../distributions/multinomial.h" -#include "../../distributions/truncated_normal.h" #include "../../metrics/mean_metric.h" #include "../../networks/constants.h" #include "../../networks_utils/print_module.h" @@ -152,10 +152,10 @@ namespace arenai::agent { const torch::Tensor &old_log_probs, const torch::Tensor &advantages) const { const auto device = actor->parameters().back().device(); - const auto [mu, sigma, discrete_proba] = actor->act(vision, proprioception); + const auto [mode, concentration, discrete_proba] = actor->act(vision, proprioception); const auto curr_continuous_log_probs = - truncated_normal_log_pdf(continuous_actions, mu, sigma).sum(-1, true); + beta_law_log_proba(continuous_actions, mode, concentration).sum(-1, true); const auto clamped_proba = torch::clamp(discrete_proba, EPSILON, 1.0 - EPSILON); const auto curr_discrete_log_probs = @@ -167,7 +167,7 @@ namespace arenai::agent { const auto ratio = torch::exp(log_ratio); - const auto continuous_entropy = truncated_normal_entropy(mu, sigma); + const auto continuous_entropy = beta_law_entropy(mode, concentration); const auto discrete_entropy = multinomial_entropy(discrete_proba); const auto kl_per_row = (ratio - 1.f - log_ratio).flatten(); diff --git a/arenai_agent/src/agents/sac/sac_agent.cpp b/arenai_agent/src/agents/sac/sac_agent.cpp index b2d3963c..a8d60f45 100644 --- a/arenai_agent/src/agents/sac/sac_agent.cpp +++ b/arenai_agent/src/agents/sac/sac_agent.cpp @@ -4,8 +4,8 @@ #include "./sac_agent.h" +#include "../../distributions/beta_law.h" #include "../../distributions/multinomial.h" -#include "../../distributions/truncated_normal.h" #include "../../networks_utils/torch_converter.h" #include "../../networks_utils/torch_loader.h" @@ -41,13 +41,13 @@ namespace arenai::agent { actor->train(false); const auto &[vision, sensors] = state; - const auto &[mu, sigma, discrete_proba] = actor->act(vision, sensors); + const auto &[mode, concentration, discrete_proba] = actor->act(vision, sensors); if (sample) { - action.continuous_action = truncated_normal_sample(mu, sigma); + action.continuous_action = beta_law_sample(mode, concentration); action.discrete_action = multinomial_sample(discrete_proba); } else { - action.continuous_action = mu; + action.continuous_action = beta_law_mode_action(mode); action.discrete_action = multinomial_max_action(discrete_proba); } } diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index 19592a20..abf12d1b 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -6,8 +6,8 @@ #include +#include "../../distributions/beta_law.h" #include "../../distributions/multinomial.h" -#include "../../distributions/truncated_normal.h" #include "../../metrics/last_metric.h" #include "../../metrics/mean_metric.h" #include "../../metrics/std_metric.h" @@ -108,10 +108,11 @@ namespace arenai::agent { torch::NoGradGuard no_grad; const auto soft_value = [&](const TorchState &s) { - const auto [mu, sigma, discrete_proba] = actor->act(s.vision, s.proprioception); + const auto [mode, concentration, discrete_proba] = + actor->act(s.vision, s.proprioception); - const auto continuous_action = truncated_normal_sample(mu, sigma); - const auto continuous_entropy = truncated_normal_entropy(mu, sigma); + const auto continuous_action = beta_law_sample(mode, concentration); + const auto continuous_entropy = beta_law_entropy(mode, concentration); const auto discrete_entropy = multinomial_entropy(discrete_proba); @@ -166,11 +167,11 @@ namespace arenai::agent { soft_update(target_critic_2, critic_2, tau); // policy - const auto [curr_mu, curr_sigma, curr_discrete_proba] = + const auto [curr_mode, curr_concentration, curr_discrete_proba] = actor->act(state.vision, state.proprioception); - const auto curr_continuous_action = truncated_normal_sample(curr_mu, curr_sigma); - const auto curr_continuous_entropy = truncated_normal_entropy(curr_mu, curr_sigma); + const auto curr_continuous_action = beta_law_sample(curr_mode, curr_concentration); + const auto curr_continuous_entropy = beta_law_entropy(curr_mode, curr_concentration); const auto curr_discrete_entropy = multinomial_entropy(curr_discrete_proba); diff --git a/arenai_agent/src/networks/actor.cpp b/arenai_agent/src/networks/actor.cpp index 32e83bc9..f9fef12c 100644 --- a/arenai_agent/src/networks/actor.cpp +++ b/arenai_agent/src/networks/actor.cpp @@ -31,14 +31,15 @@ namespace arenai::agent { torch::nn::LayerNorm(torch::nn::LayerNormOptions({hidden_size_sensors})), torch::nn::SiLU()))), head(register_module("head", torch::nn::Sequential())), - mu(register_module( - "mu", torch::nn::Sequential( - torch::nn::Linear(hidden_sizes.back(), nb_continuous_actions), - torch::nn::Tanh()))), - sigma(register_module( - "sigma", torch::nn::Sequential( - torch::nn::Linear(hidden_sizes.back(), nb_continuous_actions), - std::make_shared(SIGMA_MIN, SIGMA_MAX)))), + mode(register_module( + "mode", torch::nn::Sequential( + torch::nn::Linear(hidden_sizes.back(), nb_continuous_actions), + torch::nn::Sigmoid()))), + concentration(register_module( + "concentration", + torch::nn::Sequential( + torch::nn::Linear(hidden_sizes.back(), nb_continuous_actions), + std::make_shared(CONCENTRATION_MIN, CONCENTRATION_MAX)))), discrete(register_module( "discrete", torch::nn::Sequential( torch::nn::Linear(hidden_sizes.back(), nb_discrete_actions), @@ -64,8 +65,10 @@ namespace arenai::agent { sensors_encoder->apply(init_hidden_weights); head->apply(init_hidden_weights); - mu->apply(init_mu_output_weights); - sigma->apply([initial_sigma](Module &m) { init_sigma_output_weights(m, initial_sigma); }); + // zero bias + sigmoid puts the initial mode at the action-range center + mode->apply(init_mu_output_weights); + concentration->apply( + [initial_sigma](Module &m) { init_concentration_output_weights(m, initial_sigma); }); discrete->apply([initial_fire_proba](Module &m) { init_discrete_output_weights(m, initial_fire_proba); @@ -77,8 +80,8 @@ namespace arenai::agent { auto sensors_encoded = sensors_encoder->forward(sensors); auto encoded = head->forward(torch::cat({vision_encoded, sensors_encoded}, 1)); return { - .mu = mu->forward(encoded), - .sigma = sigma->forward(encoded), + .mode = mode->forward(encoded), + .concentration = concentration->forward(encoded), .discrete = discrete->forward(encoded)}; } diff --git a/arenai_agent/src/networks/actor.h b/arenai_agent/src/networks/actor.h index 549eddcf..46ef6e05 100644 --- a/arenai_agent/src/networks/actor.h +++ b/arenai_agent/src/networks/actor.h @@ -14,8 +14,10 @@ namespace arenai::agent { struct ActorRawOutput { - torch::Tensor mu; - torch::Tensor sigma; + // Beta distribution on [-1, 1]: mode in [0, 1] (on the underlying [0, 1] + // support) and concentration κ = α + β + torch::Tensor mode; + torch::Tensor concentration; torch::Tensor discrete; }; @@ -36,8 +38,8 @@ namespace arenai::agent { torch::nn::Sequential head; - torch::nn::Sequential mu; - torch::nn::Sequential sigma; + torch::nn::Sequential mode; + torch::nn::Sequential concentration; torch::nn::Sequential discrete; }; diff --git a/arenai_agent/tests/src/tests_agents/probe_act_sample.cpp b/arenai_agent/tests/src/tests_agents/probe_act_sample.cpp index b3de4585..0ad3a4e7 100644 --- a/arenai_agent/tests/src/tests_agents/probe_act_sample.cpp +++ b/arenai_agent/tests/src/tests_agents/probe_act_sample.cpp @@ -3,7 +3,7 @@ #include #include -#include +#include #include using namespace arenai; @@ -28,21 +28,21 @@ TEST(ProbeActSample, SacStatsBiasedMu) { h, w, nb_sensors, nb_cont, nb_disc, 8, std::vector{16}, std::vector>{{3, 4}}, std::vector{2}, 0.3f, 0.1f); - // push mu away from 0 : mu ~ tanh(bias) + // push the mode away from the center : mode ~ sigmoid(bias) { torch::NoGradGuard guard; for (auto &p: actor->named_parameters()) - if (p.key().find("mu") != std::string::npos + if (p.key().find("mode") != std::string::npos && p.key().find("bias") != std::string::npos) - p.value().copy_(torch::tensor({std::atanh(0.7f), std::atanh(-0.4f)})); + p.value().copy_(torch::tensor({std::log(0.85f / 0.15f), std::log(0.3f / 0.7f)})); } const auto agent = std::make_shared(actor, torch::Device(torch::kCPU)); const auto state = probe_state(1, h, w, nb_sensors); torch::NoGradGuard guard; - const auto [mu, sigma, disc] = actor->act(state.vision, state.proprioception); - std::cout << "mu: " << mu << "\nsigma: " << sigma << std::endl; + const auto [mode, concentration, disc] = actor->act(state.vision, state.proprioception); + std::cout << "mode: " << mode << "\nconcentration: " << concentration << std::endl; constexpr int N = 4000; std::vector conts; @@ -71,8 +71,9 @@ TEST(ProbeActSample, PpoLogProbs) { collector->on_episode_end(state); torch::NoGradGuard guard; - const auto [mu, sigma, disc] = actor->act(state.vision, state.proprioception); - const auto expected_cont = truncated_normal_log_pdf(continuous_action, mu, sigma).sum(-1, true); + const auto [mode, concentration, disc] = actor->act(state.vision, state.proprioception); + const auto expected_cont = + beta_law_log_proba(continuous_action, mode, concentration).sum(-1, true); const auto expected_disc = (discrete_action * torch::log(torch::clamp(disc, 1e-8, 1.0 - 1e-8))).sum(-1, true); @@ -96,14 +97,16 @@ TEST(ProbeActSample, SacStats) { // raw actor output torch::NoGradGuard guard; - const auto [mu, sigma, disc] = actor->act(state.vision, state.proprioception); - std::cout << "mu: " << mu << "\nsigma: " << sigma << "\ndisc_proba: " << disc << std::endl; + const auto [mode, concentration, disc] = actor->act(state.vision, state.proprioception); + std::cout << "mode: " << mode << "\nconcentration: " << concentration + << "\ndisc_proba: " << disc << std::endl; // deterministic const auto [continuous_action, discrete_action] = agent->act(state, false); std::cout << "act(false) cont: " << continuous_action << "\nact(false) disc: " << discrete_action << std::endl; - std::cout << "cont == mu ? " << torch::allclose(continuous_action, mu) << std::endl; + std::cout << "cont == mode action ? " + << torch::allclose(continuous_action, beta_law_mode_action(mode)) << std::endl; // stochastic stats constexpr int N = 2000; diff --git a/arenai_agent/tests/src/tests_networks/tests_actor.cpp b/arenai_agent/tests/src/tests_networks/tests_actor.cpp index 08c0b673..32711e68 100644 --- a/arenai_agent/tests/src/tests_networks/tests_actor.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_actor.cpp @@ -3,6 +3,7 @@ // #include +#include #include @@ -27,19 +28,21 @@ TEST_P(ActorTestParam, TestActorAct) { torch::TensorOptions().dtype(torch::kUInt8)); const auto sensors = torch::randn({batch_size, sensors_nb}); - const auto [mu, sigma, discrete] = actor.act(image, sensors); + const auto [mode, concentration, discrete] = actor.act(image, sensors); - ASSERT_EQ(mu.ndimension(), 2); - ASSERT_EQ(mu.size(0), batch_size); - ASSERT_EQ(mu.size(1), cont_actions_nb); + ASSERT_EQ(mode.ndimension(), 2); + ASSERT_EQ(mode.size(0), batch_size); + ASSERT_EQ(mode.size(1), cont_actions_nb); ASSERT_TRUE( - torch::all(torch::logical_and(torch::ge(mu, -1.0), torch::le(mu, 1.0))).item()); - - ASSERT_EQ(sigma.ndimension(), 2); - ASSERT_EQ(sigma.size(0), batch_size); - ASSERT_EQ(sigma.size(1), cont_actions_nb); - ASSERT_TRUE( - torch::all(torch::logical_and(torch::gt(sigma, 0.0), torch::le(sigma, 1.0))).item()); + torch::all(torch::logical_and(torch::ge(mode, 0.0), torch::le(mode, 1.0))).item()); + + ASSERT_EQ(concentration.ndimension(), 2); + ASSERT_EQ(concentration.size(0), batch_size); + ASSERT_EQ(concentration.size(1), cont_actions_nb); + ASSERT_TRUE(torch::all(torch::logical_and( + torch::ge(concentration, CONCENTRATION_MIN), + torch::le(concentration, CONCENTRATION_MAX))) + .item()); ASSERT_EQ(discrete.ndimension(), 2); ASSERT_EQ(discrete.size(0), batch_size); diff --git a/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp b/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp index 983873df..db681013 100644 --- a/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp @@ -102,8 +102,8 @@ TEST_F(ActorGradientTest, GradientFlowsThroughActor) { const auto vision = torch::randint(0, 255, {batch, 3, height, width}, torch::kUInt8); const auto sensors = torch::randn({batch, nb_sensors}); - const auto [mu, sigma, discrete] = actor.act(vision, sensors); - const auto loss = mu.sum() + sigma.sum() + discrete.sum(); + const auto [mode, concentration, discrete] = actor.act(vision, sensors); + const auto loss = mode.sum() + concentration.sum() + discrete.sum(); loss.backward(); @@ -133,8 +133,8 @@ TEST_F(ActorGradientTest, ActorWeightsChangeAfterOptimStep) { const auto vision = torch::randint(0, 255, {batch, 3, height, width}, torch::kUInt8); const auto sensors = torch::randn({batch, nb_sensors}); - const auto [mu, sigma, discrete] = actor.act(vision, sensors); - const auto loss = -(mu.sum() + sigma.log().sum()); + const auto [mode, concentration, discrete] = actor.act(vision, sensors); + const auto loss = -(mode.sum() + concentration.log().sum()); optimizer.zero_grad(); loss.backward(); From c446071bb093f5180f86aef9987ea4b4d8af3ebe Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 17 Sep 2026 16:23:22 +0200 Subject: [PATCH 29/57] refactor --- arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp index 2dd1165c..7092d2be 100644 --- a/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo_liquid/liquid_ppo_trainer.cpp @@ -284,11 +284,11 @@ namespace arenai::agent { const torch::Tensor &vision, const torch::Tensor &proprioception, const torch::Tensor &hidden, const torch::Tensor &returns, const torch::Tensor &valids) const { - const auto out = critic->value_sequence(vision, proprioception, hidden); + const auto [value, next_x] = critic->value_sequence(vision, proprioception, hidden); // recurrence done: fold time into rows and keep the live transitions only const auto valid_idx = torch::nonzero(valids.flatten(0, 1).squeeze(-1)).squeeze(-1); - const auto values = out.value.flatten(0, 1).index_select(0, valid_idx); + const auto values = value.flatten(0, 1).index_select(0, valid_idx); const auto valid_returns = returns.flatten(0, 1).index_select(0, valid_idx); const auto critic_loss = torch::mse_loss(values, valid_returns, at::Reduction::Mean); From 18990b2271204f3ef16eaccedad7bf0fb3c32ad3 Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 17 Sep 2026 18:08:24 +0200 Subject: [PATCH 30/57] refactor liquid network (fusion of classes) --- arenai_agent/python/.gitignore | 1 + .../convert_liquid_cell_fusion.ipynb | 274 ++++++++++++++++++ .../src/networks/recurrent/liquid_actor.cpp | 2 +- .../src/networks/recurrent/liquid_actor.h | 4 +- .../{liquid_recurrent.cpp => liquid_cell.cpp} | 57 ++-- .../{liquid_recurrent.h => liquid_cell.h} | 38 +-- .../src/networks/recurrent/liquid_critic.cpp | 2 +- .../src/networks/recurrent/liquid_critic.h | 4 +- ...liquid_recurrent.h => tests_liquid_cell.h} | 11 +- ...id_recurrent.cpp => tests_liquid_cell.cpp} | 70 ++--- 10 files changed, 345 insertions(+), 118 deletions(-) create mode 100644 arenai_agent/python/notebooks/convert_liquid_cell_fusion.ipynb rename arenai_agent/src/networks/recurrent/{liquid_recurrent.cpp => liquid_cell.cpp} (76%) rename arenai_agent/src/networks/recurrent/{liquid_recurrent.h => liquid_cell.h} (74%) rename arenai_agent/tests/include/arenai_agent_tests/tests_networks/{tests_liquid_recurrent.h => tests_liquid_cell.h} (60%) rename arenai_agent/tests/src/tests_networks/{tests_liquid_recurrent.cpp => tests_liquid_cell.cpp} (69%) diff --git a/arenai_agent/python/.gitignore b/arenai_agent/python/.gitignore index c18dd8d8..6bbbd0dc 100644 --- a/arenai_agent/python/.gitignore +++ b/arenai_agent/python/.gitignore @@ -1 +1,2 @@ __pycache__/ +.ipynb_checkpoints/ diff --git a/arenai_agent/python/notebooks/convert_liquid_cell_fusion.ipynb b/arenai_agent/python/notebooks/convert_liquid_cell_fusion.ipynb new file mode 100644 index 00000000..513e892d --- /dev/null +++ b/arenai_agent/python/notebooks/convert_liquid_cell_fusion.ipynb @@ -0,0 +1,274 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "intro", + "metadata": {}, + "source": [ + "# Liquid checkpoint conversion — `LiquidRecurrent` / `LiquidCell` fusion\n", + "\n", + "The classes `LiquidRecurrent` and `LiquidCell` were merged into a single `LiquidCell`\n", + "(`arenai_agent/src/networks/recurrent/liquid_cell.h`). The intermediate `cell` submodule\n", + "disappeared, so every parameter saved under `liquid.cell.*` now lives under `liquid.*`:\n", + "\n", + "| old key | new key |\n", + "|---|---|\n", + "| `liquid.cell.a` | `liquid.a` |\n", + "| `liquid.cell.raw_tau` | `liquid.raw_tau` |\n", + "| `liquid.cell.f.*` | `liquid.f.*` |\n", + "| `liquid.to_output.*` | unchanged |\n", + "\n", + "This notebook reads an `actor.pt` saved **before** the fusion (via\n", + "`torch::serialize::OutputArchive`), remaps the keys and writes a checkpoint loadable by the\n", + "fused C++ code (`torch::serialize::InputArchive`). The same mapping applies to `critic.pt`.\n", + "\n", + "Notes:\n", + "- the parameter **order** is unchanged by the fusion, so `actor_optim.pt` / `critic_optim.pt`\n", + " need no conversion;\n", + "- requires `torch` (`pip install torch`, the CPU wheel is enough — it is not part of the\n", + " `arenai_agent/python` requirements)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "paths", + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "from pathlib import Path\n", + "\n", + "import torch\n", + "from torch import nn\n", + "\n", + "OLD_CHECKPOINT = Path(\n", + " \"/home/samuel/Téléchargements/ppo_liquid_56/save_56/actor.pt\"\n", + ")\n", + "# written under a `converted/` sibling folder, same file name\n", + "NEW_CHECKPOINT = OLD_CHECKPOINT.parent / \"converted\" / OLD_CHECKPOINT.name" + ] + }, + { + "cell_type": "markdown", + "id": "load-md", + "metadata": {}, + "source": [ + "## Read the old state dict\n", + "\n", + "A module saved by LibTorch's `OutputArchive` is a TorchScript archive: `torch.jit.load`\n", + "gives back the full module hierarchy with its named parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "load", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "vision_encoder.cnn.0.weight (8, 3, 3, 3)\n", + "vision_encoder.cnn.1.weight (8,)\n", + "vision_encoder.cnn.1.bias (8,)\n", + "vision_encoder.cnn.3.weight (16, 8, 3, 3)\n", + "vision_encoder.cnn.4.weight (16,)\n", + "vision_encoder.cnn.4.bias (16,)\n", + "vision_encoder.cnn.6.weight (24, 16, 3, 3)\n", + "vision_encoder.cnn.7.weight (24,)\n", + "vision_encoder.cnn.7.bias (24,)\n", + "vision_encoder.cnn.9.weight (32, 24, 3, 3)\n", + "vision_encoder.cnn.10.weight (32,)\n", + "vision_encoder.cnn.10.bias (32,)\n", + "vision_encoder.cnn.12.weight (48, 32, 3, 3)\n", + "vision_encoder.cnn.13.weight (48,)\n", + "vision_encoder.cnn.13.bias (48,)\n", + "vision_encoder.cnn.15.weight (64, 48, 3, 3)\n", + "vision_encoder.cnn.16.weight (64,)\n", + "vision_encoder.cnn.16.bias (64,)\n", + "sensors_encoder.0.weight (128, 134)\n", + "sensors_encoder.1.weight (128,)\n", + "sensors_encoder.1.bias (128,)\n", + "liquid.cell.a (1, 128)\n", + "liquid.cell.raw_tau (1, 128)\n", + "liquid.cell.f.biases (1, 128)\n", + "liquid.cell.f.weights.weight (128, 640)\n", + "liquid.cell.f.recurrent_weights.weight (128, 128)\n", + "liquid.to_output.0.weight (128, 128)\n", + "liquid.to_output.1.weight (128,)\n", + "liquid.to_output.1.bias (128,)\n", + "mode.0.weight (4, 128)\n", + "mode.0.bias (4,)\n", + "concentration.0.weight (4, 128)\n", + "concentration.0.bias (4,)\n", + "discrete.0.weight (2, 128)\n", + "discrete.0.bias (2,)\n" + ] + } + ], + "source": [ + "old_module = torch.jit.load(str(OLD_CHECKPOINT), map_location=\"cpu\")\n", + "\n", + "old_params = {name: p.detach().clone() for name, p in old_module.named_parameters()}\n", + "old_buffers = dict(old_module.named_buffers())\n", + "assert not old_buffers, f\"unexpected buffers, extend the notebook: {sorted(old_buffers)}\"\n", + "\n", + "for name, p in old_params.items():\n", + " print(f\"{name:45s} {tuple(p.shape)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "convert-md", + "metadata": {}, + "source": [ + "## Remap and rebuild\n", + "\n", + "The C++ `Module::load` walks the archive by **module hierarchy**, and requires every\n", + "serialized submodule to exist — including parameterless ones (SiLU, Sigmoid, LayerNorm\n", + "positions in the `Sequential`s...). So the converted archive must replicate the full module\n", + "tree, not just the parameter paths: the old `liquid.cell` node is dropped and its children\n", + "are re-attached directly to `liquid`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "convert", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "written: /home/samuel/Téléchargements/ppo_liquid_56/save_56/converted/actor.pt\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/samuel/CLionProjects/ArenAI/.venv/lib/python3.14/site-packages/torch/jit/_script.py:1485: FutureWarning: `torch.jit.script` is not supported in Python 3.14+ and may break. Please switch to `torch.compile` or `torch.export`.\n", + " warnings.warn(\n", + "/home/samuel/CLionProjects/ArenAI/.venv/lib/python3.14/site-packages/torch/jit/_serialization.py:83: FutureWarning: `torch.jit.save` is not supported in Python 3.14+ and may break. Please switch to `torch.export`.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "def convert(name: str) -> str:\n", + " return re.sub(r\"^liquid\\.cell\\.\", \"liquid.\", name)\n", + "\n", + "\n", + "class Node(nn.Module):\n", + " def forward(self) -> None:\n", + " return None\n", + "\n", + "\n", + "def ensure_path(root: Node, dotted: str) -> Node:\n", + " module = root\n", + " for part in dotted.split(\".\"):\n", + " if part not in module._modules:\n", + " module.add_module(part, Node())\n", + " module = module._modules[part]\n", + " return module\n", + "\n", + "\n", + "root = Node()\n", + "for name, _ in old_module.named_modules():\n", + " if name and name != \"liquid.cell\":\n", + " ensure_path(root, convert(name))\n", + "for name, tensor in old_params.items():\n", + " *path, leaf = convert(name).split(\".\")\n", + " ensure_path(root, \".\".join(path)).register_parameter(leaf, nn.Parameter(tensor))\n", + "\n", + "NEW_CHECKPOINT.parent.mkdir(parents=True, exist_ok=True)\n", + "torch.jit.save(torch.jit.script(root), str(NEW_CHECKPOINT))\n", + "print(f\"written: {NEW_CHECKPOINT}\")" + ] + }, + { + "cell_type": "markdown", + "id": "verify-md", + "metadata": {}, + "source": [ + "## Verify the round trip" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "verify", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OK — 35 parameters remapped\n", + " liquid.a (1, 128)\n", + " liquid.f.biases (1, 128)\n", + " liquid.f.recurrent_weights.weight (128, 128)\n", + " liquid.f.weights.weight (128, 640)\n", + " liquid.raw_tau (1, 128)\n", + " liquid.to_output.0.weight (128, 128)\n", + " liquid.to_output.1.bias (128,)\n", + " liquid.to_output.1.weight (128,)\n" + ] + } + ], + "source": [ + "reloaded = torch.jit.load(str(NEW_CHECKPOINT), map_location=\"cpu\")\n", + "\n", + "new_params = dict(reloaded.named_parameters())\n", + "expected = {convert(name): t for name, t in old_params.items()}\n", + "\n", + "assert set(new_params) == set(expected), set(new_params) ^ set(expected)\n", + "for name, t in expected.items():\n", + " assert torch.equal(new_params[name], t), name\n", + "\n", + "# structure the C++ InputArchive will walk\n", + "liquid = reloaded.liquid\n", + "for attr in (\"a\", \"raw_tau\", \"f\", \"to_output\"):\n", + " assert hasattr(liquid, attr), attr\n", + "assert not hasattr(liquid, \"cell\")\n", + "\n", + "print(f\"OK — {len(new_params)} parameters remapped\")\n", + "for name in sorted(new_params):\n", + " if name.startswith(\"liquid\"):\n", + " print(f\" {name:35s} {tuple(new_params[name].shape)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98c45cbd-59e2-4ae7-8918-54e749510121", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.cpp b/arenai_agent/src/networks/recurrent/liquid_actor.cpp index 1cb18978..cdebe851 100644 --- a/arenai_agent/src/networks/recurrent/liquid_actor.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_actor.cpp @@ -31,7 +31,7 @@ namespace arenai::agent { torch::nn::LayerNorm(torch::nn::LayerNormOptions({hidden_size_sensors})), torch::nn::SiLU()))), liquid(register_module( - "liquid", std::make_shared( + "liquid", std::make_shared( neuron_number, hidden_size_sensors + vision_encoder->get_output_size(), neuron_number, unfolding_steps, [](const torch::Tensor &t) { return torch::silu(t); }, delta_t))), diff --git a/arenai_agent/src/networks/recurrent/liquid_actor.h b/arenai_agent/src/networks/recurrent/liquid_actor.h index 54535160..8b243501 100644 --- a/arenai_agent/src/networks/recurrent/liquid_actor.h +++ b/arenai_agent/src/networks/recurrent/liquid_actor.h @@ -10,7 +10,7 @@ #include #include "../vision.h" -#include "./liquid_recurrent.h" +#include "./liquid_cell.h" namespace arenai::agent { @@ -52,7 +52,7 @@ namespace arenai::agent { std::shared_ptr vision_encoder; torch::nn::Sequential sensors_encoder; - std::shared_ptr liquid; + std::shared_ptr liquid; torch::nn::Sequential mode; torch::nn::Sequential concentration; diff --git a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp b/arenai_agent/src/networks/recurrent/liquid_cell.cpp similarity index 76% rename from arenai_agent/src/networks/recurrent/liquid_recurrent.cpp rename to arenai_agent/src/networks/recurrent/liquid_cell.cpp index a2dcb63b..1d265dd1 100644 --- a/arenai_agent/src/networks/recurrent/liquid_recurrent.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_cell.cpp @@ -2,7 +2,7 @@ // Created by samuel on 06/09/2026. // -#include "./liquid_recurrent.h" +#include "./liquid_cell.h" #include "../../networks_utils/init.h" @@ -36,42 +36,16 @@ torch::Tensor CellModel::forward(const torch::Tensor &x_t, const torch::Tensor & /* * Liquid cell */ + LiquidCell::LiquidCell( - const int neuron_number, const int input_size, const int unfolding_steps, + const int neuron_number, const int input_size, const int output_size, const int unfolding_steps, const std::function &activation_function, const float delta_t) : a(register_parameter("a", torch::ones({1, neuron_number}))), raw_tau(register_parameter("raw_tau", torch::zeros({1, neuron_number}))), f(register_module( "f", std::make_shared(neuron_number, input_size, activation_function))), - unfolding_steps(unfolding_steps), delta_t(delta_t) {} - -torch::Tensor LiquidCell::forward(const torch::Tensor &x_t, const torch::Tensor &input_t) { - auto x_t_next = x_t; - const auto curr_delta_t = delta_t / static_cast(unfolding_steps); - - for (int i = 0; i < unfolding_steps; ++i) { - const auto f_output = f->forward(x_t_next, input_t); - x_t_next = (x_t_next + curr_delta_t * f_output * a) - / (1.0 + curr_delta_t * (1.0 / tau() + f_output)); - } - - return x_t_next; -} - -torch::Tensor LiquidCell::tau() const { return torch::nn::functional::softplus(raw_tau) + 1e-3; } - -/* - * Liquid recurrent - */ - -LiquidRecurrent::LiquidRecurrent( - int neuron_number, int input_size, int output_size, int unfolding_steps, - const std::function &activation_function, float delta_t) - : cell(register_module( - "cell", std::make_shared( - neuron_number, input_size, unfolding_steps, activation_function, delta_t))), - neuron_number(neuron_number), + unfolding_steps(unfolding_steps), delta_t(delta_t), neuron_number(neuron_number), to_output(register_module( "to_output", torch::nn::Sequential( @@ -80,7 +54,7 @@ LiquidRecurrent::LiquidRecurrent( torch::nn::LayerNormOptions({output_size}).elementwise_affine(true)), torch::nn::SiLU()))) {} -torch::Tensor LiquidRecurrent::forward(const torch::Tensor &inputs) { +torch::Tensor LiquidCell::forward(const torch::Tensor &inputs) { TORCH_CHECK( inputs.sizes().size() == 3, "Processed input needs to have 3 dimensions (Batch, Time, Features)"); @@ -106,11 +80,26 @@ torch::Tensor LiquidRecurrent::forward(const torch::Tensor &inputs) { } std::tuple -LiquidRecurrent::forward_step(const torch::Tensor &x_t, const torch::Tensor &input_t) { - const auto x_t_next = cell->forward(x_t, input_t); +LiquidCell::forward_step(const torch::Tensor &x_t, const torch::Tensor &input_t) { + const auto x_t_next = next_x(x_t, input_t); return {to_output->forward(x_t_next), x_t_next}; } -torch::Tensor LiquidRecurrent::get_first_x(int batch_size) { +torch::Tensor LiquidCell::next_x(const torch::Tensor &x_t, const torch::Tensor &input_t) const { + auto x_t_next = x_t; + const auto curr_delta_t = delta_t / static_cast(unfolding_steps); + + for (int i = 0; i < unfolding_steps; ++i) { + const auto f_output = f->forward(x_t_next, input_t); + x_t_next = (x_t_next + curr_delta_t * f_output * a) + / (1.0 + curr_delta_t * (1.0 / tau() + f_output)); + } + + return x_t_next; +} + +torch::Tensor LiquidCell::tau() const { return torch::nn::functional::softplus(raw_tau) + 1e-3; } + +torch::Tensor LiquidCell::get_first_x(int batch_size) { return 1e-1 * torch::randn({batch_size, neuron_number}, parameters().back().device()); } diff --git a/arenai_agent/src/networks/recurrent/liquid_recurrent.h b/arenai_agent/src/networks/recurrent/liquid_cell.h similarity index 74% rename from arenai_agent/src/networks/recurrent/liquid_recurrent.h rename to arenai_agent/src/networks/recurrent/liquid_cell.h index 037c98e9..ca1f6cb5 100644 --- a/arenai_agent/src/networks/recurrent/liquid_recurrent.h +++ b/arenai_agent/src/networks/recurrent/liquid_cell.h @@ -2,8 +2,8 @@ // Created by samuel on 06/09/2026. // -#ifndef ARENAI_LIQUID_RECURRENT_H -#define ARENAI_LIQUID_RECURRENT_H +#ifndef ARENAI_LIQUID_CELL_H +#define ARENAI_LIQUID_CELL_H #include @@ -27,27 +27,6 @@ namespace arenai::agent { class LiquidCell : public torch::nn::Module { public: LiquidCell( - int neuron_number, int input_size, int unfolding_steps, - const std::function &activation_function, - float delta_t); - - torch::Tensor forward(const torch::Tensor &x_t, const torch::Tensor &input_t); - - private: - torch::Tensor a; - torch::Tensor raw_tau; - - std::shared_ptr f; - - int unfolding_steps; - float delta_t; - - torch::Tensor tau() const; - }; - - class LiquidRecurrent : public torch::nn::Module { - public: - LiquidRecurrent( int neuron_number, int input_size, int output_size, int unfolding_steps, const std::function &activation_function, float delta_t); @@ -61,11 +40,20 @@ namespace arenai::agent { torch::Tensor get_first_x(int batch_size); private: - std::shared_ptr cell; + torch::Tensor a; + torch::Tensor raw_tau; + + std::shared_ptr f; + + int unfolding_steps; + float delta_t; int neuron_number; torch::nn::Sequential to_output; + + torch::Tensor tau() const; + torch::Tensor next_x(const torch::Tensor &x_t, const torch::Tensor &input_t) const; }; }// namespace arenai::agent -#endif//ARENAI_LIQUID_RECURRENT_H +#endif//ARENAI_LIQUID_CELL_H diff --git a/arenai_agent/src/networks/recurrent/liquid_critic.cpp b/arenai_agent/src/networks/recurrent/liquid_critic.cpp index 5f9ca239..2a70fbb0 100644 --- a/arenai_agent/src/networks/recurrent/liquid_critic.cpp +++ b/arenai_agent/src/networks/recurrent/liquid_critic.cpp @@ -27,7 +27,7 @@ namespace arenai::agent { torch::nn::LayerNorm(torch::nn::LayerNormOptions({hidden_size_sensors})), torch::nn::SiLU()))), liquid(register_module( - "liquid", std::make_shared( + "liquid", std::make_shared( neuron_number, hidden_size_sensors + vision_encoder->get_output_size(), neuron_number, unfolding_steps, [](const torch::Tensor &t) { return torch::silu(t); }, delta_t))), diff --git a/arenai_agent/src/networks/recurrent/liquid_critic.h b/arenai_agent/src/networks/recurrent/liquid_critic.h index e513b908..d5286525 100644 --- a/arenai_agent/src/networks/recurrent/liquid_critic.h +++ b/arenai_agent/src/networks/recurrent/liquid_critic.h @@ -10,7 +10,7 @@ #include #include "../vision.h" -#include "./liquid_recurrent.h" +#include "./liquid_cell.h" namespace arenai::agent { @@ -45,7 +45,7 @@ namespace arenai::agent { std::shared_ptr vision_encoder; torch::nn::Sequential sensors_encoder; - std::shared_ptr liquid; + std::shared_ptr liquid; torch::nn::Linear to_value; diff --git a/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_liquid_recurrent.h b/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_liquid_cell.h similarity index 60% rename from arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_liquid_recurrent.h rename to arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_liquid_cell.h index 45fbc427..554c1192 100644 --- a/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_liquid_recurrent.h +++ b/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_liquid_cell.h @@ -2,8 +2,8 @@ // Created by samuel on 06/09/2026. // -#ifndef ARENAI_TESTS_LIQUID_RECURRENT_H -#define ARENAI_TESTS_LIQUID_RECURRENT_H +#ifndef ARENAI_TESTS_LIQUID_CELL_H +#define ARENAI_TESTS_LIQUID_CELL_H #include @@ -17,11 +17,8 @@ typedef int TimeSteps; class CellModelTestParam : public testing::TestWithParam> {}; -class LiquidCellTestParam : public testing::TestWithParam< - std::tuple> {}; - -class LiquidRecurrentTestParam +class LiquidCellTestParam : public testing::TestWithParam< std::tuple> {}; -#endif//ARENAI_TESTS_LIQUID_RECURRENT_H +#endif//ARENAI_TESTS_LIQUID_CELL_H diff --git a/arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp b/arenai_agent/tests/src/tests_networks/tests_liquid_cell.cpp similarity index 69% rename from arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp rename to arenai_agent/tests/src/tests_networks/tests_liquid_cell.cpp index 08ba0195..13132d11 100644 --- a/arenai_agent/tests/src/tests_networks/tests_liquid_recurrent.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_liquid_cell.cpp @@ -2,9 +2,9 @@ // Created by samuel on 06/09/2026. // -#include +#include -#include +#include using namespace arenai; using namespace arenai::agent; @@ -71,59 +71,37 @@ INSTANTIATE_TEST_SUITE_P( * Liquid cell */ -TEST_P(LiquidCellTestParam, OutputShape) { - const auto [neuron_number, input_size, unfolding_steps, batch_size] = GetParam(); +TEST_P(LiquidCellTestParam, StepOutputShape) { + const auto [neuron_number, input_size, output_size, unfolding_steps, batch_size, time_steps] = + GetParam(); - LiquidCell liquid_cell(neuron_number, input_size, unfolding_steps, tanh_activation, DELTA_T); + LiquidCell liquid_cell( + neuron_number, input_size, output_size, unfolding_steps, tanh_activation, DELTA_T); const auto x_t = torch::randn({batch_size, neuron_number}); const auto input_t = torch::randn({batch_size, input_size}); - const auto output = liquid_cell.forward(x_t, input_t); + const auto [output, x_t_next] = liquid_cell.forward_step(x_t, input_t); ASSERT_EQ(output.ndimension(), 2); ASSERT_EQ(output.size(0), batch_size); - ASSERT_EQ(output.size(1), neuron_number); -} - -TEST_P(LiquidCellTestParam, GradientFlows) { - const auto [neuron_number, input_size, unfolding_steps, batch_size] = GetParam(); + ASSERT_EQ(output.size(1), output_size); - LiquidCell liquid_cell(neuron_number, input_size, unfolding_steps, tanh_activation, DELTA_T); - - const auto x_t = torch::randn({batch_size, neuron_number}, torch::requires_grad()); - const auto input_t = torch::randn({batch_size, input_size}, torch::requires_grad()); - - liquid_cell.forward(x_t, input_t).sum().backward(); - - assert_all_parameters_receive_gradient(liquid_cell); - - ASSERT_TRUE(x_t.grad().defined()); - ASSERT_GT(x_t.grad().abs().sum().item(), 0.f); - ASSERT_TRUE(input_t.grad().defined()); - ASSERT_GT(input_t.grad().abs().sum().item(), 0.f); + ASSERT_EQ(x_t_next.ndimension(), 2); + ASSERT_EQ(x_t_next.size(0), batch_size); + ASSERT_EQ(x_t_next.size(1), neuron_number); } -INSTANTIATE_TEST_SUITE_P( - TestLiquidCell, LiquidCellTestParam, - testing::Combine( - testing::Values(2, 8, 16), testing::Values(1, 3, 12), testing::Values(1, 2, 6), - testing::Values(1, 4))); - -/* - * Liquid recurrent - */ - -TEST_P(LiquidRecurrentTestParam, OutputShape) { +TEST_P(LiquidCellTestParam, OutputShape) { const auto [neuron_number, input_size, output_size, unfolding_steps, batch_size, time_steps] = GetParam(); - LiquidRecurrent liquid_recurrent( + LiquidCell liquid_cell( neuron_number, input_size, output_size, unfolding_steps, tanh_activation, DELTA_T); const auto inputs = torch::randn({batch_size, time_steps, input_size}); - const auto output = liquid_recurrent.forward(inputs); + const auto output = liquid_cell.forward(inputs); ASSERT_EQ(output.ndimension(), 3); ASSERT_EQ(output.size(0), batch_size); @@ -131,7 +109,7 @@ TEST_P(LiquidRecurrentTestParam, OutputShape) { ASSERT_EQ(output.size(2), output_size); } -TEST_P(LiquidRecurrentTestParam, GradientFlows) { +TEST_P(LiquidCellTestParam, GradientFlows) { const auto [neuron_number, input_size, output_size, unfolding_steps, batch_size, time_steps] = GetParam(); @@ -139,14 +117,14 @@ TEST_P(LiquidRecurrentTestParam, GradientFlows) { // which blocks any gradient from reaching the layers before it if (output_size == 1) GTEST_SKIP() << "LayerNorm({1}) zeroes the signal, no gradient flows"; - LiquidRecurrent liquid_recurrent( + LiquidCell liquid_cell( neuron_number, input_size, output_size, unfolding_steps, tanh_activation, DELTA_T); const auto inputs = torch::randn({batch_size, time_steps, input_size}, torch::requires_grad()); - liquid_recurrent.forward(inputs).sum().backward(); + liquid_cell.forward(inputs).sum().backward(); - assert_all_parameters_receive_gradient(liquid_recurrent); + assert_all_parameters_receive_gradient(liquid_cell); ASSERT_TRUE(inputs.grad().defined()); ASSERT_TRUE(torch::all(torch::isfinite(inputs.grad())).item()); @@ -156,15 +134,15 @@ TEST_P(LiquidRecurrentTestParam, GradientFlows) { << "Some time steps received no gradient: " << per_step_grad; } -TEST(LiquidRecurrentEdge, RejectsNon3DInput) { - LiquidRecurrent liquid_recurrent(4, 3, 2, 2, tanh_activation, DELTA_T); +TEST(LiquidCellEdge, RejectsNon3DInput) { + LiquidCell liquid_cell(4, 3, 2, 2, tanh_activation, DELTA_T); - EXPECT_THROW(liquid_recurrent.forward(torch::randn({2, 3})), c10::Error); - EXPECT_THROW(liquid_recurrent.forward(torch::randn({2, 5, 3, 1})), c10::Error); + EXPECT_THROW(liquid_cell.forward(torch::randn({2, 3})), c10::Error); + EXPECT_THROW(liquid_cell.forward(torch::randn({2, 5, 3, 1})), c10::Error); } INSTANTIATE_TEST_SUITE_P( - TestLiquidRecurrent, LiquidRecurrentTestParam, + TestLiquidCell, LiquidCellTestParam, testing::Combine( testing::Values(2, 8, 16), testing::Values(1, 3, 12), testing::Values(1, 2, 6), testing::Values(1, 3), testing::Values(1, 4), testing::Values(1, 2, 5))); From 99376ebf8501f31d1aeebec9749753229e17c51f Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 17 Sep 2026 18:28:07 +0200 Subject: [PATCH 31/57] add trained model (ppo liquid), next checkpoints are coming soon (training is computing) ! --- .../src/core/agent_loading_checker.cpp | 4 +-- arenai_desktop/src/main.cpp | 4 +-- .../config.json | 23 ++++++++++++------ .../ppo_liquid_400/save_56/actor.pt | Bin 0 -> 789982 bytes .../ppo_train_386/save_30/actor.pt | Bin 5072820 -> 0 bytes .../ppo_train_386/save_66/actor.pt | Bin 5072820 -> 0 bytes 6 files changed, 19 insertions(+), 12 deletions(-) rename resources/trained_models/{ppo_train_386 => ppo_liquid_400}/config.json (62%) create mode 100644 resources/trained_models/ppo_liquid_400/save_56/actor.pt delete mode 100644 resources/trained_models/ppo_train_386/save_30/actor.pt delete mode 100644 resources/trained_models/ppo_train_386/save_66/actor.pt diff --git a/arenai_desktop/src/core/agent_loading_checker.cpp b/arenai_desktop/src/core/agent_loading_checker.cpp index 3aa3a0e3..32fdb38b 100644 --- a/arenai_desktop/src/core/agent_loading_checker.cpp +++ b/arenai_desktop/src/core/agent_loading_checker.cpp @@ -15,8 +15,8 @@ namespace arenai::desktop { agent::AgentAlgorithm to_agent_algorithm(const gui::AiAlgorithm algorithm) { switch (algorithm) { case gui::AiAlgorithm::Sac: return agent::SAC; - case gui::AiAlgorithm::PpoLiquid: return agent::PPO_LIQUID; - default: return agent::PPO; + case gui::AiAlgorithm::Ppo: return agent::PPO; + default: return agent::PPO_LIQUID; } } diff --git a/arenai_desktop/src/main.cpp b/arenai_desktop/src/main.cpp index 8f087a98..cbb23d7c 100644 --- a/arenai_desktop/src/main.cpp +++ b/arenai_desktop/src/main.cpp @@ -46,8 +46,8 @@ int main(const int argc, char **argv) { {.window_width = parser.get("--window_width"), .window_height = parser.get("--window_height"), .resources_folder = resources_folder}, - {.state_dict_folder = resources_folder / "trained_models" / "ppo_train_386" / "save_66", - .config_json = resources_folder / "trained_models" / "ppo_train_386" / "config.json", + {.state_dict_folder = resources_folder / "trained_models" / "ppo_train_400" / "save_56", + .config_json = resources_folder / "trained_models" / "ppo_train_400" / "config.json", .cuda = parser.get("--cuda")}); return 0; diff --git a/resources/trained_models/ppo_train_386/config.json b/resources/trained_models/ppo_liquid_400/config.json similarity index 62% rename from resources/trained_models/ppo_train_386/config.json rename to resources/trained_models/ppo_liquid_400/config.json index 9179dcb9..89b2150a 100644 --- a/resources/trained_models/ppo_train_386/config.json +++ b/resources/trained_models/ppo_liquid_400/config.json @@ -1,11 +1,11 @@ { "agent": { - "actor_hidden_sizes": "[1024, 512]", "actor_learning_rate": "0.0001", + "chunk_size": "30", "clip_epsilon": "0.2", - "continuous_target_entropy": "0.4", - "critic_hidden_sizes": "[1024, 512]", + "continuous_target_entropy": "-0.88", "critic_learning_rate": "0.0003", + "delta_t": "0.0333333", "discrete_target_entropy_factor": "0.2", "epochs": "2", "gae_lambda": "0.99", @@ -17,15 +17,22 @@ "initial_sigma": "0.5", "metric_window_size": "256", "minibatch_size": "1024", + "neuron_number": "128", "rollout_size": "900", "target_kl": "0.05", + "unfolding_steps": "6", "vision_channels": "[(3, 8), (8, 16), (16, 24), (24, 32), (32, 48), (48, 64)]" }, "environment": { - "final_spawn_height": 500.0, - "final_spawn_width": 500.0, - "initial_spawn_height": 500.0, - "initial_spawn_width": 500.0, + "curriculum_boundary_proba": 0.20000000298023224, + "curriculum_delta": 50.0, + "curriculum_probe_window": 24, + "curriculum_ratio_high": 0.03999999910593033, + "curriculum_ratio_low": 0.019999999552965164, + 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