refacto: bot directories

bot/Cargo.toml

@@ -7,19 +7,19 @@ edition = "2021"
 
 [[bin]]
 name = "train_dqn_burn_valid"
-path = "src/dqn/burnrl_valid/main.rs"
+path = "src/burnrl/dqn_valid/main.rs"
 
 [[bin]]
 name = "train_dqn_burn_big"
-path = "src/dqn/burnrl_big/main.rs"
+path = "src/burnrl/dqn_big/main.rs"
 
 [[bin]]
 name = "train_dqn_burn"
-path = "src/dqn/burnrl/main.rs"
+path = "src/burnrl/dqn/main.rs"
 
 [[bin]]
 name = "train_dqn_simple"
-path = "src/dqn/simple/main.rs"
+path = "src/dqn_simple/main.rs"
 
 [dependencies]
 pretty_assertions = "1.4.0"

bot/src/burnrl/dqn/dqn_model.rs

@@ -1,5 +1,5 @@
-use crate::dqn::burnrl_big::environment::TrictracEnvironment;
+use crate::burnrl::dqn::utils::soft_update_linear;
-use crate::dqn::burnrl_big::utils::soft_update_linear;
+use crate::burnrl::environment::TrictracEnvironment;
 use burn::module::Module;
 use burn::nn::{Linear, LinearConfig};
 use burn::optim::AdamWConfig;
@@ -126,7 +126,7 @@ pub fn run<E: Environment + AsMut<TrictracEnvironment>, B: AutodiffBackend>(
 ) -> DQN<E, B, Net<B>> {
     // ) -> impl Agent<E> {
     let mut env = E::new(visualized);
-    env.as_mut().min_steps = conf.min_steps;
+    // env.as_mut().min_steps = conf.min_steps;
     env.as_mut().max_steps = conf.max_steps;
 
     let model = Net::<B>::new(
@@ -193,12 +193,17 @@ pub fn run<E: Environment + AsMut<TrictracEnvironment>, B: AutodiffBackend>(
 
             if snapshot.done() || episode_duration >= conf.max_steps {
                 let envmut = env.as_mut();
+                let goodmoves_ratio = ((envmut.goodmoves_count as f32 / episode_duration as f32)
+                    * 100.0)
+                    .round() as u32;
                 println!(
-                    "{{\"episode\": {episode}, \"reward\": {episode_reward:.4}, \"steps count\": {episode_duration}, \"epsilon\": {eps_threshold:.3}, \"goodmoves\": {}, \"rollpoints\":{}, \"duration\": {}}}",
+                    "{{\"episode\": {episode}, \"reward\": {episode_reward:.4}, \"steps count\": {episode_duration}, \"epsilon\": {eps_threshold:.3}, \"goodmoves\": {}, \"ratio\": {}%, \"rollpoints\":{}, \"duration\": {}}}",
                     envmut.goodmoves_count,
+                    goodmoves_ratio,
                     envmut.pointrolls_count,
                     now.elapsed().unwrap().as_secs(),
                 );
+                if goodmoves_ratio < 5 && 10 < episode {}
                 env.reset();
                 episode_done = true;
                 now = SystemTime::now();

bot/src/burnrl/dqn/main.rs

@@ -1,7 +1,8 @@
-use bot::dqn::burnrl::{
+use bot::burnrl::dqn::{
-    dqn_model, environment,
+    dqn_model,
     utils::{demo_model, load_model, save_model},
 };
+use bot::burnrl::environment;
 use burn::backend::{Autodiff, NdArray};
 use burn_rl::agent::DQN;
 use burn_rl::base::ElemType;
@@ -15,9 +16,9 @@ fn main() {
     // See also MEMORY_SIZE in dqn_model.rs : 8192
     let conf = dqn_model::DqnConfig {
         //                   defaults
-        num_episodes: 40,  // 40
+        num_episodes: 50,  // 40
         min_steps: 1000.0, // 1000 min of max steps by episode (mise à jour par la fonction)
-        max_steps: 2000,   // 1000 max steps by episode
+        max_steps: 1000,   // 1000 max steps by episode
         dense_size: 256,   // 128  neural network complexity (default 128)
         eps_start: 0.9,    // 0.9  epsilon initial value (0.9 => more exploration)
         eps_end: 0.05,     // 0.05
@@ -31,8 +32,8 @@ fn main() {
         // plus lente moins sensible aux coups de chance
         learning_rate: 0.001, // 0.001 taille du pas. Bas : plus lent, haut : risque de ne jamais
         // converger
-        batch_size: 64, // 32 nombre d'expériences passées sur lesquelles pour calcul de l'erreur moy.
+        batch_size: 128, // 32 nombre d'expériences passées sur lesquelles pour calcul de l'erreur moy.
-        clip_grad: 50.0, // 100 limite max de correction à apporter au gradient (default 100)
+        clip_grad: 70.0, // 100 limite max de correction à apporter au gradient (default 100)
     };
     println!("{conf}----------");
     let agent = dqn_model::run::<Env, Backend>(&conf, false); //true);

bot/src/burnrl/dqn/mod.rs

@@ -1,3 +1,2 @@
 pub mod dqn_model;
-pub mod environment;
 pub mod utils;

bot/src/burnrl/dqn/utils.rs

@@ -1,8 +1,6 @@
-use crate::dqn::burnrl::{
+use crate::burnrl::dqn::dqn_model;
-    dqn_model,
+use crate::burnrl::environment::{TrictracAction, TrictracEnvironment};
-    environment::{TrictracAction, TrictracEnvironment},
+use crate::training_common::get_valid_action_indices;
-};
-use crate::dqn::dqn_common::get_valid_action_indices;
 use burn::backend::{ndarray::NdArrayDevice, NdArray};
 use burn::module::{Module, Param, ParamId};
 use burn::nn::Linear;

bot/src/burnrl/dqn_big/dqn_model.rs

@@ -1,5 +1,5 @@
-use crate::dqn::burnrl::environment::TrictracEnvironment;
+use crate::burnrl::dqn_big::utils::soft_update_linear;
-use crate::dqn::burnrl::utils::soft_update_linear;
+use crate::burnrl::environment_big::TrictracEnvironment;
 use burn::module::Module;
 use burn::nn::{Linear, LinearConfig};
 use burn::optim::AdamWConfig;

bot/src/burnrl/dqn_big/main.rs

@@ -1,13 +1,14 @@
-use bot::dqn::burnrl_big::{
+use bot::burnrl::dqn_big::{
-    dqn_model, environment,
+    dqn_model,
     utils::{demo_model, load_model, save_model},
 };
+use bot::burnrl::environment_big;
 use burn::backend::{Autodiff, NdArray};
 use burn_rl::agent::DQN;
 use burn_rl::base::ElemType;
 
 type Backend = Autodiff<NdArray<ElemType>>;
-type Env = environment::TrictracEnvironment;
+type Env = environment_big::TrictracEnvironment;
 
 fn main() {
     // println!("> Entraînement");

bot/src/burnrl/dqn_big/mod.rs

@@ -1,3 +1,2 @@
 pub mod dqn_model;
-pub mod environment;
 pub mod utils;

bot/src/burnrl/dqn_big/utils.rs

@@ -1,8 +1,6 @@
-use crate::dqn::burnrl_valid::{
+use crate::burnrl::dqn_big::dqn_model;
-    dqn_model,
+use crate::burnrl::environment_big::{TrictracAction, TrictracEnvironment};
-    environment::{TrictracAction, TrictracEnvironment},
+use crate::training_common_big::get_valid_action_indices;
-};
-use crate::dqn::dqn_common::get_valid_action_indices;
 use burn::backend::{ndarray::NdArrayDevice, NdArray};
 use burn::module::{Module, Param, ParamId};
 use burn::nn::Linear;

bot/src/burnrl/dqn_valid/dqn_model.rs

@@ -1,5 +1,5 @@
-use crate::dqn::burnrl_valid::environment::TrictracEnvironment;
+use crate::burnrl::dqn_valid::utils::soft_update_linear;
-use crate::dqn::burnrl_valid::utils::soft_update_linear;
+use crate::burnrl::environment::TrictracEnvironment;
 use burn::module::Module;
 use burn::nn::{Linear, LinearConfig};
 use burn::optim::AdamWConfig;

bot/src/burnrl/dqn_valid/main.rs

@@ -1,7 +1,8 @@
-use bot::dqn::burnrl_valid::{
+use bot::burnrl::dqn_valid::{
-    dqn_model, environment,
+    dqn_model,
     utils::{demo_model, load_model, save_model},
 };
+use bot::burnrl::environment;
 use burn::backend::{Autodiff, NdArray};
 use burn_rl::agent::DQN;
 use burn_rl::base::ElemType;

bot/src/burnrl/dqn_valid/mod.rs

@@ -1,3 +1,2 @@
 pub mod dqn_model;
-pub mod environment;
 pub mod utils;

bot/src/burnrl/dqn_valid/utils.rs

@@ -1,8 +1,6 @@
-use crate::dqn::burnrl_big::{
+use crate::burnrl::dqn_valid::dqn_model;
-    dqn_model,
+use crate::burnrl::environment_valid::{TrictracAction, TrictracEnvironment};
-    environment::{TrictracAction, TrictracEnvironment},
+use crate::training_common::get_valid_action_indices;
-};
-use crate::dqn::dqn_common_big::get_valid_action_indices;
 use burn::backend::{ndarray::NdArrayDevice, NdArray};
 use burn::module::{Module, Param, ParamId};
 use burn::nn::Linear;

bot/src/burnrl/environment.rs

@@ -1,13 +1,15 @@
-use crate::dqn::dqn_common;
+use std::io::Write;
+
+use crate::training_common;
 use burn::{prelude::Backend, tensor::Tensor};
 use burn_rl::base::{Action, Environment, Snapshot, State};
 use rand::{thread_rng, Rng};
 use store::{GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage};
 
-const ERROR_REWARD: f32 = -2.12121;
+const ERROR_REWARD: f32 = -1.12121;
-const REWARD_VALID_MOVE: f32 = 2.12121;
+const REWARD_VALID_MOVE: f32 = 1.12121;
 const REWARD_RATIO: f32 = 0.01;
-const WIN_POINTS: f32 = 0.1;
+const WIN_POINTS: f32 = 1.0;
 
 /// État du jeu Trictrac pour burn-rl
 #[derive(Debug, Clone, Copy)]
@@ -89,7 +91,7 @@ pub struct TrictracEnvironment {
     current_state: TrictracState,
     episode_reward: f32,
     pub step_count: usize,
-    pub min_steps: f32,
+    pub best_ratio: f32,
     pub max_steps: usize,
     pub pointrolls_count: usize,
     pub goodmoves_count: usize,
@@ -122,7 +124,7 @@ impl Environment for TrictracEnvironment {
             current_state,
             episode_reward: 0.0,
             step_count: 0,
-            min_steps: 250.0,
+            best_ratio: 0.0,
             max_steps: 2000,
             pointrolls_count: 0,
             goodmoves_count: 0,
@@ -151,10 +153,21 @@ impl Environment for TrictracEnvironment {
         } else {
             self.goodmoves_count as f32 / self.step_count as f32
         };
+        self.best_ratio = self.best_ratio.max(self.goodmoves_ratio);
+        let warning = if self.best_ratio > 0.7 && self.goodmoves_ratio < 0.1 {
+            let path = "bot/models/logs/debug.log";
+            if let Ok(mut out) = std::fs::File::create(path) {
+                write!(out, "{:?}", self.game.history);
+            }
+            "!!!!"
+        } else {
+            ""
+        };
         println!(
-            "info: correct moves: {} ({}%)",
+            "info: correct moves: {} ({}%) {}",
             self.goodmoves_count,
-            (100.0 * self.goodmoves_ratio).round() as u32
+            (100.0 * self.goodmoves_ratio).round() as u32,
+            warning
         );
         self.step_count = 0;
         self.pointrolls_count = 0;
@@ -195,9 +208,10 @@ impl Environment for TrictracEnvironment {
         }
 
         // Vérifier si la partie est terminée
-        let max_steps = self.min_steps
+        // let max_steps = self.max_steps;
-            + (self.max_steps as f32 - self.min_steps)
+        // let max_steps = self.min_steps
-                * f32::exp((self.goodmoves_ratio - 1.0) / 0.25);
+        //     + (self.max_steps as f32 - self.min_steps)
+        //         * f32::exp((self.goodmoves_ratio - 1.0) / 0.25);
         let done = self.game.stage == Stage::Ended || self.game.determine_winner().is_some();
 
         if done {
@@ -210,7 +224,8 @@ impl Environment for TrictracEnvironment {
                 }
             }
         }
-        let terminated = done || self.step_count >= max_steps.round() as usize;
+        let terminated = done || self.step_count >= self.max_steps;
+        // let terminated = done || self.step_count >= max_steps.round() as usize;
 
         // Mettre à jour l'état
         self.current_state = TrictracState::from_game_state(&self.game);
@@ -229,8 +244,8 @@ impl Environment for TrictracEnvironment {
 
 impl TrictracEnvironment {
     /// Convertit une action burn-rl vers une action Trictrac
-    pub fn convert_action(action: TrictracAction) -> Option<dqn_common::TrictracAction> {
+    pub fn convert_action(action: TrictracAction) -> Option<training_common::TrictracAction> {
-        dqn_common::TrictracAction::from_action_index(action.index.try_into().unwrap())
+        training_common::TrictracAction::from_action_index(action.index.try_into().unwrap())
     }
 
     /// Convertit l'index d'une action au sein des actions valides vers une action Trictrac
@@ -239,8 +254,8 @@ impl TrictracEnvironment {
         &self,
         action: TrictracAction,
         game_state: &GameState,
-    ) -> Option<dqn_common::TrictracAction> {
+    ) -> Option<training_common::TrictracAction> {
-        use dqn_common::get_valid_actions;
+        use training_common::get_valid_actions;
 
         // Obtenir les actions valides dans le contexte actuel
         let valid_actions = get_valid_actions(game_state);
@@ -257,10 +272,10 @@ impl TrictracEnvironment {
     /// Exécute une action Trictrac dans le jeu
     // fn execute_action(
     //     &mut self,
-    //     action: dqn_common::TrictracAction,
+    //     action: training_common::TrictracAction,
     // ) -> Result<f32, Box<dyn std::error::Error>> {
-    fn execute_action(&mut self, action: dqn_common::TrictracAction) -> (f32, bool) {
+    fn execute_action(&mut self, action: training_common::TrictracAction) -> (f32, bool) {
-        use dqn_common::TrictracAction;
+        use training_common::TrictracAction;
 
         let mut reward = 0.0;
         let mut is_rollpoint = false;

bot/src/burnrl/environment_big.rs

@@ -1,4 +1,4 @@
-use crate::dqn::dqn_common_big;
+use crate::training_common_big;
 use burn::{prelude::Backend, tensor::Tensor};
 use burn_rl::base::{Action, Environment, Snapshot, State};
 use rand::{thread_rng, Rng};
@@ -229,8 +229,8 @@ impl Environment for TrictracEnvironment {
 
 impl TrictracEnvironment {
     /// Convertit une action burn-rl vers une action Trictrac
-    pub fn convert_action(action: TrictracAction) -> Option<dqn_common_big::TrictracAction> {
+    pub fn convert_action(action: TrictracAction) -> Option<training_common_big::TrictracAction> {
-        dqn_common_big::TrictracAction::from_action_index(action.index.try_into().unwrap())
+        training_common_big::TrictracAction::from_action_index(action.index.try_into().unwrap())
     }
 
     /// Convertit l'index d'une action au sein des actions valides vers une action Trictrac
@@ -239,8 +239,8 @@ impl TrictracEnvironment {
         &self,
         action: TrictracAction,
         game_state: &GameState,
-    ) -> Option<dqn_common_big::TrictracAction> {
+    ) -> Option<training_common_big::TrictracAction> {
-        use dqn_common_big::get_valid_actions;
+        use training_common_big::get_valid_actions;
 
         // Obtenir les actions valides dans le contexte actuel
         let valid_actions = get_valid_actions(game_state);
@@ -257,10 +257,10 @@ impl TrictracEnvironment {
     /// Exécute une action Trictrac dans le jeu
     // fn execute_action(
     //     &mut self,
-    //     action:dqn_common_big::TrictracAction,
+    //     action:training_common_big::TrictracAction,
     // ) -> Result<f32, Box<dyn std::error::Error>> {
-    fn execute_action(&mut self, action: dqn_common_big::TrictracAction) -> (f32, bool) {
+    fn execute_action(&mut self, action: training_common_big::TrictracAction) -> (f32, bool) {
-        use dqn_common_big::TrictracAction;
+        use training_common_big::TrictracAction;
 
         let mut reward = 0.0;
         let mut is_rollpoint = false;

bot/src/burnrl/environment_valid.rs

@@ -1,4 +1,4 @@
-use crate::dqn::dqn_common_big;
+use crate::training_common_big;
 use burn::{prelude::Backend, tensor::Tensor};
 use burn_rl::base::{Action, Environment, Snapshot, State};
 use rand::{thread_rng, Rng};
@@ -214,16 +214,16 @@ impl TrictracEnvironment {
     const REWARD_RATIO: f32 = 1.0;
 
     /// Convertit une action burn-rl vers une action Trictrac
-    pub fn convert_action(action: TrictracAction) -> Option<dqn_common_big::TrictracAction> {
+    pub fn convert_action(action: TrictracAction) -> Option<training_common_big::TrictracAction> {
-        dqn_common_big::TrictracAction::from_action_index(action.index.try_into().unwrap())
+        training_common_big::TrictracAction::from_action_index(action.index.try_into().unwrap())
     }
 
     /// Convertit l'index d'une action au sein des actions valides vers une action Trictrac
     fn convert_valid_action_index(
         &self,
         action: TrictracAction,
-    ) -> Option<dqn_common_big::TrictracAction> {
+    ) -> Option<training_common_big::TrictracAction> {
-        use dqn_common_big::get_valid_actions;
+        use training_common_big::get_valid_actions;
 
         // Obtenir les actions valides dans le contexte actuel
         let valid_actions = get_valid_actions(&self.game);
@@ -240,10 +240,10 @@ impl TrictracEnvironment {
     /// Exécute une action Trictrac dans le jeu
     // fn execute_action(
     //     &mut self,
-    //     action: dqn_common_big::TrictracAction,
+    //     action: training_common_big::TrictracAction,
     // ) -> Result<f32, Box<dyn std::error::Error>> {
-    fn execute_action(&mut self, action: dqn_common_big::TrictracAction) -> (f32, bool) {
+    fn execute_action(&mut self, action: training_common_big::TrictracAction) -> (f32, bool) {
-        use dqn_common_big::TrictracAction;
+        use training_common_big::TrictracAction;
 
         let mut reward = 0.0;
         let mut is_rollpoint = false;

bot/src/burnrl/mod.rs

@@ -0,0 +1,6 @@
+pub mod dqn;
+pub mod dqn_big;
+pub mod dqn_valid;
+pub mod environment;
+pub mod environment_big;
+pub mod environment_valid;

bot/src/dqn/mod.rs

@@ -1,7 +0,0 @@
-pub mod burnrl;
-pub mod burnrl_big;
-pub mod dqn_common;
-pub mod dqn_common_big;
-pub mod simple;
-
-pub mod burnrl_valid;

bot/src/dqn_simple/dqn_model.rs

@@ -1,4 +1,4 @@
-use crate::dqn::dqn_common::TrictracAction;
+use crate::training_common_big::TrictracAction;
 use serde::{Deserialize, Serialize};
 
 /// Configuration pour l'agent DQN
@@ -151,4 +151,3 @@ impl SimpleNeuralNetwork {
         Ok(network)
     }
 }
-

bot/src/dqn_simple/dqn_trainer.rs

@@ -6,7 +6,7 @@ use std::collections::VecDeque;
 use store::{GameEvent, MoveRules, PointsRules, Stage, TurnStage};
 
 use super::dqn_model::{DqnConfig, SimpleNeuralNetwork};
-use crate::dqn::dqn_common_big::{get_valid_actions, TrictracAction};
+use crate::training_common_big::{get_valid_actions, TrictracAction};
 
 /// Expérience pour le buffer de replay
 #[derive(Debug, Clone, Serialize, Deserialize)]

bot/src/dqn_simple/main.rs

@@ -1,6 +1,6 @@
-use bot::dqn::dqn_common::TrictracAction;
+use bot::dqn_simple::dqn_model::DqnConfig;
-use bot::dqn::simple::dqn_model::DqnConfig;
+use bot::dqn_simple::dqn_trainer::DqnTrainer;
-use bot::dqn::simple::dqn_trainer::DqnTrainer;
+use bot::training_common::TrictracAction;
 use std::env;
 
 fn main() -> Result<(), Box<dyn std::error::Error>> {

bot/src/lib.rs

@@ -1,5 +1,8 @@
-pub mod dqn;
+pub mod burnrl;
+pub mod dqn_simple;
 pub mod strategy;
+pub mod training_common;
+pub mod training_common_big;
 
 use log::debug;
 use store::{CheckerMove, Color, GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage};

bot/src/strategy/dqn.rs

@@ -3,8 +3,8 @@ use log::info;
 use std::path::Path;
 use store::MoveRules;
 
-use crate::dqn::dqn_common_big::{get_valid_actions, sample_valid_action, TrictracAction};
+use crate::dqn_simple::dqn_model::SimpleNeuralNetwork;
-use crate::dqn::simple::dqn_model::SimpleNeuralNetwork;
+use crate::training_common_big::{get_valid_actions, sample_valid_action, TrictracAction};
 
 /// Stratégie DQN pour le bot - ne fait que charger et utiliser un modèle pré-entraîné
 #[derive(Debug)]

bot/src/strategy/dqnburn.rs

@@ -6,8 +6,9 @@ use crate::{BotStrategy, CheckerMove, Color, GameState, PlayerId};
 use log::info;
 use store::MoveRules;
 
-use crate::dqn::burnrl::{dqn_model, environment, utils};
+use crate::burnrl::dqn::{dqn_model, utils};
-use crate::dqn::dqn_common::{get_valid_action_indices, sample_valid_action, TrictracAction};
+use crate::burnrl::environment;
+use crate::training_common::{get_valid_action_indices, sample_valid_action, TrictracAction};
 
 type DqnBurnNetwork = dqn_model::Net<NdArray<ElemType>>;