Cargo.lock

@@ -834,7 +834,7 @@ dependencies = [
  "derive-new",
  "log",
  "nvml-wrapper",
- "ratatui",
+ "ratatui 0.29.0",
  "rstest",
  "serde",
  "sysinfo",
@@ -1066,6 +1066,17 @@ dependencies = [
  "store",
 ]
 
+[[package]]
+name = "client_tui"
+version = "0.1.0"
+dependencies = [
+ "anyhow",
+ "bincode 1.3.3",
+ "crossterm",
+ "ratatui 0.28.1",
+ "store",
+]
+
 [[package]]
 name = "cmake"
 version = "0.1.54"
@@ -4403,6 +4414,27 @@ version = "0.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c3d6831663a5098ea164f89cff59c6284e95f4e3c76ce9848d4529f5ccca9bde"
 
+[[package]]
+name = "ratatui"
+version = "0.28.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "fdef7f9be5c0122f890d58bdf4d964349ba6a6161f705907526d891efabba57d"
+dependencies = [
+ "bitflags 2.9.4",
+ "cassowary",
+ "compact_str",
+ "crossterm",
+ "instability",
+ "itertools 0.13.0",
+ "lru",
+ "paste",
+ "strum 0.26.3",
+ "strum_macros 0.26.4",
+ "unicode-segmentation",
+ "unicode-truncate",
+ "unicode-width 0.1.14",
+]
+
 [[package]]
 name = "ratatui"
 version = "0.29.0"
@@ -5781,6 +5813,18 @@ dependencies = [
  "strength_reduce",
 ]
 
+[[package]]
+name = "trictrac-server"
+version = "0.1.0"
+dependencies = [
+ "bincode 1.3.3",
+ "env_logger 0.10.2",
+ "log",
+ "pico-args",
+ "renet",
+ "store",
+]
+
 [[package]]
 name = "tungstenite"
 version = "0.26.2"

Cargo.toml

@@ -1,4 +1,4 @@
 [workspace]
 resolver = "2"
 
-members = ["client_cli", "bot", "store"]
+members = ["client_tui", "client_cli", "bot", "server", "store"]

bot/src/burnrl/algos/dqn_big.rs

@@ -0,0 +1,194 @@
+use crate::burnrl::environment_big::TrictracEnvironment;
+use crate::burnrl::utils::{soft_update_linear, Config};
+use burn::backend::{ndarray::NdArrayDevice, NdArray};
+use burn::module::Module;
+use burn::nn::{Linear, LinearConfig};
+use burn::optim::AdamWConfig;
+use burn::record::{CompactRecorder, Recorder};
+use burn::tensor::activation::relu;
+use burn::tensor::backend::{AutodiffBackend, Backend};
+use burn::tensor::Tensor;
+use burn_rl::agent::DQN;
+use burn_rl::agent::{DQNModel, DQNTrainingConfig};
+use burn_rl::base::{Action, Agent, ElemType, Environment, Memory, Model, State};
+use std::time::SystemTime;
+
+#[derive(Module, Debug)]
+pub struct Net<B: Backend> {
+    linear_0: Linear<B>,
+    linear_1: Linear<B>,
+    linear_2: Linear<B>,
+}
+
+impl<B: Backend> Net<B> {
+    #[allow(unused)]
+    pub fn new(input_size: usize, dense_size: usize, output_size: usize) -> Self {
+        Self {
+            linear_0: LinearConfig::new(input_size, dense_size).init(&Default::default()),
+            linear_1: LinearConfig::new(dense_size, dense_size).init(&Default::default()),
+            linear_2: LinearConfig::new(dense_size, output_size).init(&Default::default()),
+        }
+    }
+
+    fn consume(self) -> (Linear<B>, Linear<B>, Linear<B>) {
+        (self.linear_0, self.linear_1, self.linear_2)
+    }
+}
+
+impl<B: Backend> Model<B, Tensor<B, 2>, Tensor<B, 2>> for Net<B> {
+    fn forward(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        let layer_0_output = relu(self.linear_0.forward(input));
+        let layer_1_output = relu(self.linear_1.forward(layer_0_output));
+
+        relu(self.linear_2.forward(layer_1_output))
+    }
+
+    fn infer(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        self.forward(input)
+    }
+}
+
+impl<B: Backend> DQNModel<B> for Net<B> {
+    fn soft_update(this: Self, that: &Self, tau: ElemType) -> Self {
+        let (linear_0, linear_1, linear_2) = this.consume();
+
+        Self {
+            linear_0: soft_update_linear(linear_0, &that.linear_0, tau),
+            linear_1: soft_update_linear(linear_1, &that.linear_1, tau),
+            linear_2: soft_update_linear(linear_2, &that.linear_2, tau),
+        }
+    }
+}
+
+#[allow(unused)]
+const MEMORY_SIZE: usize = 8192;
+
+type MyAgent<E, B> = DQN<E, B, Net<B>>;
+
+#[allow(unused)]
+// pub fn run<E: Environment + AsMut<TrictracEnvironment>, B: AutodiffBackend>(
+pub fn run<
+    E: Environment + AsMut<TrictracEnvironment>,
+    B: AutodiffBackend<InnerBackend = NdArray>,
+>(
+    conf: &Config,
+    visualized: bool,
+    // ) -> DQN<E, B, Net<B>> {
+) -> impl Agent<E> {
+    let mut env = E::new(visualized);
+    env.as_mut().max_steps = conf.max_steps;
+
+    let model = Net::<B>::new(
+        <<E as Environment>::StateType as State>::size(),
+        conf.dense_size,
+        <<E as Environment>::ActionType as Action>::size(),
+    );
+
+    let mut agent = MyAgent::new(model);
+
+    // let config = DQNTrainingConfig::default();
+    let config = DQNTrainingConfig {
+        gamma: conf.gamma,
+        tau: conf.tau,
+        learning_rate: conf.learning_rate,
+        batch_size: conf.batch_size,
+        clip_grad: Some(burn::grad_clipping::GradientClippingConfig::Value(
+            conf.clip_grad,
+        )),
+    };
+
+    let mut memory = Memory::<E, B, MEMORY_SIZE>::default();
+
+    let mut optimizer = AdamWConfig::new()
+        .with_grad_clipping(config.clip_grad.clone())
+        .init();
+
+    let mut policy_net = agent.model().as_ref().unwrap().clone();
+
+    let mut step = 0_usize;
+
+    for episode in 0..conf.num_episodes {
+        let mut episode_done = false;
+        let mut episode_reward: ElemType = 0.0;
+        let mut episode_duration = 0_usize;
+        let mut state = env.state();
+        let mut now = SystemTime::now();
+
+        while !episode_done {
+            let eps_threshold = conf.eps_end
+                + (conf.eps_start - conf.eps_end) * f64::exp(-(step as f64) / conf.eps_decay);
+            let action =
+                DQN::<E, B, Net<B>>::react_with_exploration(&policy_net, state, eps_threshold);
+            let snapshot = env.step(action);
+
+            episode_reward +=
+                <<E as Environment>::RewardType as Into<ElemType>>::into(snapshot.reward().clone());
+
+            memory.push(
+                state,
+                *snapshot.state(),
+                action,
+                snapshot.reward().clone(),
+                snapshot.done(),
+            );
+
+            if config.batch_size < memory.len() {
+                policy_net =
+                    agent.train::<MEMORY_SIZE>(policy_net, &memory, &mut optimizer, &config);
+            }
+
+            step += 1;
+            episode_duration += 1;
+
+            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\": {}, \"ratio\": {}%, \"rollpoints\":{}, \"duration\": {}}}",
+                    envmut.goodmoves_count,
+                    goodmoves_ratio,
+                    envmut.pointrolls_count,
+                    now.elapsed().unwrap().as_secs(),
+                );
+                env.reset();
+                episode_done = true;
+                now = SystemTime::now();
+            } else {
+                state = *snapshot.state();
+            }
+        }
+    }
+    let valid_agent = agent.valid();
+    if let Some(path) = &conf.save_path {
+        save_model(valid_agent.model().as_ref().unwrap(), path);
+    }
+    valid_agent
+}
+
+pub fn save_model(model: &Net<NdArray<ElemType>>, path: &String) {
+    let recorder = CompactRecorder::new();
+    let model_path = format!("{path}.mpk");
+    println!("info: Modèle de validation sauvegardé : {model_path}");
+    recorder
+        .record(model.clone().into_record(), model_path.into())
+        .unwrap();
+}
+
+pub fn load_model(dense_size: usize, path: &String) -> Option<Net<NdArray<ElemType>>> {
+    let model_path = format!("{path}.mpk");
+    // println!("Chargement du modèle depuis : {model_path}");
+
+    CompactRecorder::new()
+        .load(model_path.into(), &NdArrayDevice::default())
+        .map(|record| {
+            Net::new(
+                <TrictracEnvironment as Environment>::StateType::size(),
+                dense_size,
+                <TrictracEnvironment as Environment>::ActionType::size(),
+            )
+            .load_record(record)
+        })
+        .ok()
+}

bot/src/burnrl/algos/mod.rs

@@ -1,6 +1,9 @@
 pub mod dqn;
+pub mod dqn_big;
 pub mod dqn_valid;
 pub mod ppo;
+pub mod ppo_big;
 pub mod ppo_valid;
 pub mod sac;
+pub mod sac_big;
 pub mod sac_valid;

bot/src/burnrl/algos/ppo_big.rs

@@ -0,0 +1,191 @@
+use crate::burnrl::environment_big::TrictracEnvironment;
+use crate::burnrl::utils::Config;
+use burn::backend::{ndarray::NdArrayDevice, NdArray};
+use burn::module::Module;
+use burn::nn::{Initializer, Linear, LinearConfig};
+use burn::optim::AdamWConfig;
+use burn::record::{CompactRecorder, Recorder};
+use burn::tensor::activation::{relu, softmax};
+use burn::tensor::backend::{AutodiffBackend, Backend};
+use burn::tensor::Tensor;
+use burn_rl::agent::{PPOModel, PPOOutput, PPOTrainingConfig, PPO};
+use burn_rl::base::{Action, Agent, ElemType, Environment, Memory, Model, State};
+use std::env;
+use std::fs;
+use std::time::SystemTime;
+
+#[derive(Module, Debug)]
+pub struct Net<B: Backend> {
+    linear: Linear<B>,
+    linear_actor: Linear<B>,
+    linear_critic: Linear<B>,
+}
+
+impl<B: Backend> Net<B> {
+    #[allow(unused)]
+    pub fn new(input_size: usize, dense_size: usize, output_size: usize) -> Self {
+        let initializer = Initializer::XavierUniform { gain: 1.0 };
+        Self {
+            linear: LinearConfig::new(input_size, dense_size)
+                .with_initializer(initializer.clone())
+                .init(&Default::default()),
+            linear_actor: LinearConfig::new(dense_size, output_size)
+                .with_initializer(initializer.clone())
+                .init(&Default::default()),
+            linear_critic: LinearConfig::new(dense_size, 1)
+                .with_initializer(initializer)
+                .init(&Default::default()),
+        }
+    }
+}
+
+impl<B: Backend> Model<B, Tensor<B, 2>, PPOOutput<B>, Tensor<B, 2>> for Net<B> {
+    fn forward(&self, input: Tensor<B, 2>) -> PPOOutput<B> {
+        let layer_0_output = relu(self.linear.forward(input));
+        let policies = softmax(self.linear_actor.forward(layer_0_output.clone()), 1);
+        let values = self.linear_critic.forward(layer_0_output);
+
+        PPOOutput::<B>::new(policies, values)
+    }
+
+    fn infer(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        let layer_0_output = relu(self.linear.forward(input));
+        softmax(self.linear_actor.forward(layer_0_output.clone()), 1)
+    }
+}
+
+impl<B: Backend> PPOModel<B> for Net<B> {}
+#[allow(unused)]
+const MEMORY_SIZE: usize = 512;
+
+type MyAgent<E, B> = PPO<E, B, Net<B>>;
+
+#[allow(unused)]
+pub fn run<
+    E: Environment + AsMut<TrictracEnvironment>,
+    B: AutodiffBackend<InnerBackend = NdArray>,
+>(
+    conf: &Config,
+    visualized: bool,
+    // ) -> PPO<E, B, Net<B>> {
+) -> impl Agent<E> {
+    let mut env = E::new(visualized);
+    env.as_mut().max_steps = conf.max_steps;
+
+    let mut model = Net::<B>::new(
+        <<E as Environment>::StateType as State>::size(),
+        conf.dense_size,
+        <<E as Environment>::ActionType as Action>::size(),
+    );
+    let agent = MyAgent::default();
+    let config = PPOTrainingConfig {
+        gamma: conf.gamma,
+        lambda: conf.lambda,
+        epsilon_clip: conf.epsilon_clip,
+        critic_weight: conf.critic_weight,
+        entropy_weight: conf.entropy_weight,
+        learning_rate: conf.learning_rate,
+        epochs: conf.epochs,
+        batch_size: conf.batch_size,
+        clip_grad: Some(burn::grad_clipping::GradientClippingConfig::Value(
+            conf.clip_grad,
+        )),
+    };
+
+    let mut optimizer = AdamWConfig::new()
+        .with_grad_clipping(config.clip_grad.clone())
+        .init();
+    let mut memory = Memory::<E, B, MEMORY_SIZE>::default();
+    for episode in 0..conf.num_episodes {
+        let mut episode_done = false;
+        let mut episode_reward = 0.0;
+        let mut episode_duration = 0_usize;
+        let mut now = SystemTime::now();
+
+        env.reset();
+        while !episode_done {
+            let state = env.state();
+            if let Some(action) = MyAgent::<E, _>::react_with_model(&state, &model) {
+                let snapshot = env.step(action);
+                episode_reward += <<E as Environment>::RewardType as Into<ElemType>>::into(
+                    snapshot.reward().clone(),
+                );
+
+                memory.push(
+                    state,
+                    *snapshot.state(),
+                    action,
+                    snapshot.reward().clone(),
+                    snapshot.done(),
+                );
+
+                episode_duration += 1;
+                episode_done = snapshot.done() || episode_duration >= conf.max_steps;
+            }
+        }
+        println!(
+            "{{\"episode\": {episode}, \"reward\": {episode_reward:.4}, \"steps count\": {episode_duration}, \"duration\": {}}}",
+                    now.elapsed().unwrap().as_secs(),
+        );
+
+        now = SystemTime::now();
+        model = MyAgent::train::<MEMORY_SIZE>(model, &memory, &mut optimizer, &config);
+        memory.clear();
+    }
+
+    if let Some(path) = &conf.save_path {
+        let device = NdArrayDevice::default();
+        let recorder = CompactRecorder::new();
+        let tmp_path = env::temp_dir().join("tmp_model.mpk");
+
+        // Save the trained model (backend B) to a temporary file
+        recorder
+            .record(model.clone().into_record(), tmp_path.clone())
+            .expect("Failed to save temporary model");
+
+        // Create a new model instance with the target backend (NdArray)
+        let model_to_save: Net<NdArray<ElemType>> = Net::new(
+            <<E as Environment>::StateType as State>::size(),
+            conf.dense_size,
+            <<E as Environment>::ActionType as Action>::size(),
+        );
+
+        // Load the record from the temporary file into the new model
+        let record = recorder
+            .load(tmp_path.clone(), &device)
+            .expect("Failed to load temporary model");
+        let model_with_loaded_weights = model_to_save.load_record(record);
+
+        // Clean up the temporary file
+        fs::remove_file(tmp_path).expect("Failed to remove temporary model file");
+
+        save_model(&model_with_loaded_weights, path);
+    }
+    agent.valid(model)
+}
+
+pub fn save_model(model: &Net<NdArray<ElemType>>, path: &String) {
+    let recorder = CompactRecorder::new();
+    let model_path = format!("{path}.mpk");
+    println!("info: Modèle de validation sauvegardé : {model_path}");
+    recorder
+        .record(model.clone().into_record(), model_path.into())
+        .unwrap();
+}
+
+pub fn load_model(dense_size: usize, path: &String) -> Option<Net<NdArray<ElemType>>> {
+    let model_path = format!("{path}.mpk");
+    // println!("Chargement du modèle depuis : {model_path}");
+
+    CompactRecorder::new()
+        .load(model_path.into(), &NdArrayDevice::default())
+        .map(|record| {
+            Net::new(
+                <TrictracEnvironment as Environment>::StateType::size(),
+                dense_size,
+                <TrictracEnvironment as Environment>::ActionType::size(),
+            )
+            .load_record(record)
+        })
+        .ok()
+}

bot/src/burnrl/algos/sac_big.rs

@@ -0,0 +1,222 @@
+use crate::burnrl::environment_big::TrictracEnvironment;
+use crate::burnrl::utils::{soft_update_linear, Config};
+use burn::backend::{ndarray::NdArrayDevice, NdArray};
+use burn::module::Module;
+use burn::nn::{Linear, LinearConfig};
+use burn::optim::AdamWConfig;
+use burn::record::{CompactRecorder, Recorder};
+use burn::tensor::activation::{relu, softmax};
+use burn::tensor::backend::{AutodiffBackend, Backend};
+use burn::tensor::Tensor;
+use burn_rl::agent::{SACActor, SACCritic, SACNets, SACOptimizer, SACTrainingConfig, SAC};
+use burn_rl::base::{Action, Agent, ElemType, Environment, Memory, Model, State};
+use std::time::SystemTime;
+
+#[derive(Module, Debug)]
+pub struct Actor<B: Backend> {
+    linear_0: Linear<B>,
+    linear_1: Linear<B>,
+    linear_2: Linear<B>,
+}
+
+impl<B: Backend> Actor<B> {
+    pub fn new(input_size: usize, dense_size: usize, output_size: usize) -> Self {
+        Self {
+            linear_0: LinearConfig::new(input_size, dense_size).init(&Default::default()),
+            linear_1: LinearConfig::new(dense_size, dense_size).init(&Default::default()),
+            linear_2: LinearConfig::new(dense_size, output_size).init(&Default::default()),
+        }
+    }
+}
+
+impl<B: Backend> Model<B, Tensor<B, 2>, Tensor<B, 2>> for Actor<B> {
+    fn forward(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        let layer_0_output = relu(self.linear_0.forward(input));
+        let layer_1_output = relu(self.linear_1.forward(layer_0_output));
+
+        softmax(self.linear_2.forward(layer_1_output), 1)
+    }
+
+    fn infer(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        self.forward(input)
+    }
+}
+
+impl<B: Backend> SACActor<B> for Actor<B> {}
+
+#[derive(Module, Debug)]
+pub struct Critic<B: Backend> {
+    linear_0: Linear<B>,
+    linear_1: Linear<B>,
+    linear_2: Linear<B>,
+}
+
+impl<B: Backend> Critic<B> {
+    pub fn new(input_size: usize, dense_size: usize, output_size: usize) -> Self {
+        Self {
+            linear_0: LinearConfig::new(input_size, dense_size).init(&Default::default()),
+            linear_1: LinearConfig::new(dense_size, dense_size).init(&Default::default()),
+            linear_2: LinearConfig::new(dense_size, output_size).init(&Default::default()),
+        }
+    }
+
+    fn consume(self) -> (Linear<B>, Linear<B>, Linear<B>) {
+        (self.linear_0, self.linear_1, self.linear_2)
+    }
+}
+
+impl<B: Backend> Model<B, Tensor<B, 2>, Tensor<B, 2>> for Critic<B> {
+    fn forward(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        let layer_0_output = relu(self.linear_0.forward(input));
+        let layer_1_output = relu(self.linear_1.forward(layer_0_output));
+
+        self.linear_2.forward(layer_1_output)
+    }
+
+    fn infer(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        self.forward(input)
+    }
+}
+
+impl<B: Backend> SACCritic<B> for Critic<B> {
+    fn soft_update(this: Self, that: &Self, tau: ElemType) -> Self {
+        let (linear_0, linear_1, linear_2) = this.consume();
+
+        Self {
+            linear_0: soft_update_linear(linear_0, &that.linear_0, tau),
+            linear_1: soft_update_linear(linear_1, &that.linear_1, tau),
+            linear_2: soft_update_linear(linear_2, &that.linear_2, tau),
+        }
+    }
+}
+
+#[allow(unused)]
+const MEMORY_SIZE: usize = 4096;
+
+type MyAgent<E, B> = SAC<E, B, Actor<B>>;
+
+#[allow(unused)]
+pub fn run<
+    E: Environment + AsMut<TrictracEnvironment>,
+    B: AutodiffBackend<InnerBackend = NdArray>,
+>(
+    conf: &Config,
+    visualized: bool,
+) -> impl Agent<E> {
+    let mut env = E::new(visualized);
+    env.as_mut().max_steps = conf.max_steps;
+    let state_dim = <<E as Environment>::StateType as State>::size();
+    let action_dim = <<E as Environment>::ActionType as Action>::size();
+
+    let actor = Actor::<B>::new(state_dim, conf.dense_size, action_dim);
+    let critic_1 = Critic::<B>::new(state_dim, conf.dense_size, action_dim);
+    let critic_2 = Critic::<B>::new(state_dim, conf.dense_size, action_dim);
+    let mut nets = SACNets::<B, Actor<B>, Critic<B>>::new(actor, critic_1, critic_2);
+
+    let mut agent = MyAgent::default();
+
+    let config = SACTrainingConfig {
+        gamma: conf.gamma,
+        tau: conf.tau,
+        learning_rate: conf.learning_rate,
+        min_probability: conf.min_probability,
+        batch_size: conf.batch_size,
+        clip_grad: Some(burn::grad_clipping::GradientClippingConfig::Value(
+            conf.clip_grad,
+        )),
+    };
+
+    let mut memory = Memory::<E, B, MEMORY_SIZE>::default();
+
+    let optimizer_config = AdamWConfig::new().with_grad_clipping(config.clip_grad.clone());
+
+    let mut optimizer = SACOptimizer::new(
+        optimizer_config.clone().init(),
+        optimizer_config.clone().init(),
+        optimizer_config.clone().init(),
+        optimizer_config.init(),
+    );
+
+    let mut step = 0_usize;
+
+    for episode in 0..conf.num_episodes {
+        let mut episode_done = false;
+        let mut episode_reward = 0.0;
+        let mut episode_duration = 0_usize;
+        let mut state = env.state();
+        let mut now = SystemTime::now();
+
+        while !episode_done {
+            if let Some(action) = MyAgent::<E, _>::react_with_model(&state, &nets.actor) {
+                let snapshot = env.step(action);
+
+                episode_reward += <<E as Environment>::RewardType as Into<ElemType>>::into(
+                    snapshot.reward().clone(),
+                );
+
+                memory.push(
+                    state,
+                    *snapshot.state(),
+                    action,
+                    snapshot.reward().clone(),
+                    snapshot.done(),
+                );
+
+                if config.batch_size < memory.len() {
+                    nets = agent.train::<MEMORY_SIZE, _>(nets, &memory, &mut optimizer, &config);
+                }
+
+                step += 1;
+                episode_duration += 1;
+
+                if snapshot.done() || episode_duration >= conf.max_steps {
+                    env.reset();
+                    episode_done = true;
+
+                    println!(
+                        "{{\"episode\": {episode}, \"reward\": {episode_reward:.4}, \"steps count\": {episode_duration}, \"duration\": {}}}",
+                    now.elapsed().unwrap().as_secs()
+                    );
+                    now = SystemTime::now();
+                } else {
+                    state = *snapshot.state();
+                }
+            }
+        }
+    }
+
+    let valid_agent = agent.valid(nets.actor);
+    if let Some(path) = &conf.save_path {
+        if let Some(model) = valid_agent.model() {
+            save_model(model, path);
+        }
+    }
+    valid_agent
+}
+
+pub fn save_model(model: &Actor<NdArray<ElemType>>, path: &String) {
+    let recorder = CompactRecorder::new();
+    let model_path = format!("{path}.mpk");
+    println!("info: Modèle de validation sauvegardé : {model_path}");
+    recorder
+        .record(model.clone().into_record(), model_path.into())
+        .unwrap();
+}
+
+pub fn load_model(dense_size: usize, path: &String) -> Option<Actor<NdArray<ElemType>>> {
+    let model_path = format!("{path}.mpk");
+    // println!("Chargement du modèle depuis : {model_path}");
+
+    CompactRecorder::new()
+        .load(model_path.into(), &NdArrayDevice::default())
+        .map(|record| {
+            Actor::new(
+                <TrictracEnvironment as Environment>::StateType::size(),
+                dense_size,
+                <TrictracEnvironment as Environment>::ActionType::size(),
+            )
+            .load_record(record)
+        })
+        .ok()
+}
+

bot/src/burnrl/environment.rs

@@ -6,10 +6,10 @@ 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 = -1.0012121;
+const ERROR_REWARD: f32 = -1.12121;
-const REWARD_VALID_MOVE: f32 = 1.0012121;
+const REWARD_VALID_MOVE: f32 = 1.12121;
-const REWARD_RATIO: f32 = 0.1;
+const REWARD_RATIO: f32 = 0.01;
-const WIN_POINTS: f32 = 100.0;
+const WIN_POINTS: f32 = 1.0;
 
 /// État du jeu Trictrac pour burn-rl
 #[derive(Debug, Clone, Copy)]
@@ -285,7 +285,7 @@ impl TrictracEnvironment {
         if let Some(event) = action.to_event(&self.game) {
             if self.game.validate(&event) {
                 self.game.consume(&event);
-                // reward += REWARD_VALID_MOVE;
+                reward += REWARD_VALID_MOVE;
                 // Simuler le résultat des dés après un Roll
                 if matches!(action, TrictracAction::Roll) {
                     let mut rng = thread_rng();
@@ -312,11 +312,9 @@ impl TrictracEnvironment {
                 // on annule les précédents reward
                 // et on indique une valeur reconnaissable pour statistiques
                 reward = ERROR_REWARD;
-                self.game.mark_points_for_bot_training(self.opponent_id, 1);
             }
         } else {
             reward = ERROR_REWARD;
-            self.game.mark_points_for_bot_training(self.opponent_id, 1);
         }
 
         (reward, is_rollpoint)

bot/src/burnrl/environment_big.rs

@@ -0,0 +1,469 @@
+use crate::training_common_big;
+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 REWARD_VALID_MOVE: f32 = 2.12121;
+const REWARD_RATIO: f32 = 0.01;
+const WIN_POINTS: f32 = 0.1;
+
+/// État du jeu Trictrac pour burn-rl
+#[derive(Debug, Clone, Copy)]
+pub struct TrictracState {
+    pub data: [i8; 36], // Représentation vectorielle de l'état du jeu
+}
+
+impl State for TrictracState {
+    type Data = [i8; 36];
+
+    fn to_tensor<B: Backend>(&self) -> Tensor<B, 1> {
+        Tensor::from_floats(self.data, &B::Device::default())
+    }
+
+    fn size() -> usize {
+        36
+    }
+}
+
+impl TrictracState {
+    /// Convertit un GameState en TrictracState
+    pub fn from_game_state(game_state: &GameState) -> Self {
+        let state_vec = game_state.to_vec();
+        let mut data = [0; 36];
+
+        // Copier les données en s'assurant qu'on ne dépasse pas la taille
+        let copy_len = state_vec.len().min(36);
+        data[..copy_len].copy_from_slice(&state_vec[..copy_len]);
+
+        TrictracState { data }
+    }
+}
+
+/// Actions possibles dans Trictrac pour burn-rl
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct TrictracAction {
+    // u32 as required by burn_rl::base::Action type
+    pub index: u32,
+}
+
+impl Action for TrictracAction {
+    fn random() -> Self {
+        use rand::{thread_rng, Rng};
+        let mut rng = thread_rng();
+        TrictracAction {
+            index: rng.gen_range(0..Self::size() as u32),
+        }
+    }
+
+    fn enumerate() -> Vec<Self> {
+        (0..Self::size() as u32)
+            .map(|index| TrictracAction { index })
+            .collect()
+    }
+
+    fn size() -> usize {
+        1252
+    }
+}
+
+impl From<u32> for TrictracAction {
+    fn from(index: u32) -> Self {
+        TrictracAction { index }
+    }
+}
+
+impl From<TrictracAction> for u32 {
+    fn from(action: TrictracAction) -> u32 {
+        action.index
+    }
+}
+
+/// Environnement Trictrac pour burn-rl
+#[derive(Debug)]
+pub struct TrictracEnvironment {
+    pub game: GameState,
+    active_player_id: PlayerId,
+    opponent_id: PlayerId,
+    current_state: TrictracState,
+    episode_reward: f32,
+    pub step_count: usize,
+    pub max_steps: usize,
+    pub pointrolls_count: usize,
+    pub goodmoves_count: usize,
+    pub goodmoves_ratio: f32,
+    pub visualized: bool,
+}
+
+impl Environment for TrictracEnvironment {
+    type StateType = TrictracState;
+    type ActionType = TrictracAction;
+    type RewardType = f32;
+
+    fn new(visualized: bool) -> Self {
+        let mut game = GameState::new(false);
+
+        // Ajouter deux joueurs
+        game.init_player("DQN Agent");
+        game.init_player("Opponent");
+        let player1_id = 1;
+        let player2_id = 2;
+
+        // Commencer la partie
+        game.consume(&GameEvent::BeginGame { goes_first: 1 });
+
+        let current_state = TrictracState::from_game_state(&game);
+        TrictracEnvironment {
+            game,
+            active_player_id: player1_id,
+            opponent_id: player2_id,
+            current_state,
+            episode_reward: 0.0,
+            step_count: 0,
+            max_steps: 2000,
+            pointrolls_count: 0,
+            goodmoves_count: 0,
+            goodmoves_ratio: 0.0,
+            visualized,
+        }
+    }
+
+    fn state(&self) -> Self::StateType {
+        self.current_state
+    }
+
+    fn reset(&mut self) -> Snapshot<Self> {
+        // Réinitialiser le jeu
+        self.game = GameState::new(false);
+        self.game.init_player("DQN Agent");
+        self.game.init_player("Opponent");
+
+        // Commencer la partie
+        self.game.consume(&GameEvent::BeginGame { goes_first: 1 });
+
+        self.current_state = TrictracState::from_game_state(&self.game);
+        self.episode_reward = 0.0;
+        self.goodmoves_ratio = if self.step_count == 0 {
+            0.0
+        } else {
+            self.goodmoves_count as f32 / self.step_count as f32
+        };
+        println!(
+            "info: correct moves: {} ({}%)",
+            self.goodmoves_count,
+            (100.0 * self.goodmoves_ratio).round() as u32
+        );
+        self.step_count = 0;
+        self.pointrolls_count = 0;
+        self.goodmoves_count = 0;
+
+        Snapshot::new(self.current_state, 0.0, false)
+    }
+
+    fn step(&mut self, action: Self::ActionType) -> Snapshot<Self> {
+        self.step_count += 1;
+
+        // Convertir l'action burn-rl vers une action Trictrac
+        let trictrac_action = Self::convert_action(action);
+
+        let mut reward = 0.0;
+        let is_rollpoint;
+
+        // Exécuter l'action si c'est le tour de l'agent DQN
+        if self.game.active_player_id == self.active_player_id {
+            if let Some(action) = trictrac_action {
+                (reward, is_rollpoint) = self.execute_action(action);
+                if is_rollpoint {
+                    self.pointrolls_count += 1;
+                }
+                if reward != ERROR_REWARD {
+                    self.goodmoves_count += 1;
+                    // println!("{str_action}");
+                }
+            } else {
+                // Action non convertible, pénalité
+                reward = -0.5;
+            }
+        }
+
+        // Faire jouer l'adversaire (stratégie simple)
+        while self.game.active_player_id == self.opponent_id && self.game.stage != Stage::Ended {
+            // print!(":");
+            reward += self.play_opponent_if_needed();
+        }
+
+        // Vérifier si la partie est terminée
+        // let max_steps = self.max_steps
+        // let max_steps = self.min_steps
+        //     + (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 {
+            // Récompense finale basée sur le résultat
+            if let Some(winner_id) = self.game.determine_winner() {
+                if winner_id == self.active_player_id {
+                    reward += WIN_POINTS; // Victoire
+                } else {
+                    reward -= WIN_POINTS; // Défaite
+                }
+            }
+        }
+        let terminated = done || self.step_count >= self.max_steps;
+
+        // Mettre à jour l'état
+        self.current_state = TrictracState::from_game_state(&self.game);
+        self.episode_reward += reward;
+        if self.visualized && terminated {
+            println!(
+                "Episode terminé. Récompense totale: {:.2}, Étapes: {}",
+                self.episode_reward, self.step_count
+            );
+        }
+
+        Snapshot::new(self.current_state, reward, terminated)
+    }
+}
+
+impl TrictracEnvironment {
+    /// Convertit une action burn-rl vers une action Trictrac
+    pub fn convert_action(action: TrictracAction) -> Option<training_common_big::TrictracAction> {
+        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
+    #[allow(dead_code)]
+    fn convert_valid_action_index(
+        &self,
+        action: TrictracAction,
+        game_state: &GameState,
+    ) -> Option<training_common_big::TrictracAction> {
+        use training_common_big::get_valid_actions;
+
+        // Obtenir les actions valides dans le contexte actuel
+        let valid_actions = get_valid_actions(game_state);
+
+        if valid_actions.is_empty() {
+            return None;
+        }
+
+        // Mapper l'index d'action sur une action valide
+        let action_index = (action.index as usize) % valid_actions.len();
+        Some(valid_actions[action_index].clone())
+    }
+
+    /// Exécute une action Trictrac dans le jeu
+    // fn execute_action(
+    //     &mut self,
+    //     action:training_common_big::TrictracAction,
+    // ) -> Result<f32, Box<dyn std::error::Error>> {
+    fn execute_action(&mut self, action: training_common_big::TrictracAction) -> (f32, bool) {
+        use training_common_big::TrictracAction;
+
+        let mut reward = 0.0;
+        let mut is_rollpoint = false;
+        let mut need_roll = false;
+
+        let event = match action {
+            TrictracAction::Roll => {
+                // Lancer les dés
+                need_roll = true;
+                Some(GameEvent::Roll {
+                    player_id: self.active_player_id,
+                })
+            }
+            // TrictracAction::Mark => {
+            //     // Marquer des points
+            //     let points = self.game.
+            //     reward += 0.1 * points as f32;
+            //     Some(GameEvent::Mark {
+            //         player_id: self.active_player_id,
+            //         points,
+            //     })
+            // }
+            TrictracAction::Go => {
+                // Continuer après avoir gagné un trou
+                Some(GameEvent::Go {
+                    player_id: self.active_player_id,
+                })
+            }
+            TrictracAction::Move {
+                dice_order,
+                from1,
+                from2,
+            } => {
+                // Effectuer un mouvement
+                let (dice1, dice2) = if dice_order {
+                    (self.game.dice.values.0, self.game.dice.values.1)
+                } else {
+                    (self.game.dice.values.1, self.game.dice.values.0)
+                };
+                let mut to1 = from1 + dice1 as usize;
+                let mut to2 = from2 + dice2 as usize;
+
+                // Gestion prise de coin par puissance
+                let opp_rest_field = 13;
+                if to1 == opp_rest_field && to2 == opp_rest_field {
+                    to1 -= 1;
+                    to2 -= 1;
+                }
+
+                let checker_move1 = store::CheckerMove::new(from1, to1).unwrap_or_default();
+                let checker_move2 = store::CheckerMove::new(from2, to2).unwrap_or_default();
+
+                Some(GameEvent::Move {
+                    player_id: self.active_player_id,
+                    moves: (checker_move1, checker_move2),
+                })
+            }
+        };
+
+        // Appliquer l'événement si valide
+        if let Some(event) = event {
+            if self.game.validate(&event) {
+                self.game.consume(&event);
+                reward += REWARD_VALID_MOVE;
+                // Simuler le résultat des dés après un Roll
+                // if matches!(action, TrictracAction::Roll) {
+                if need_roll {
+                    let mut rng = thread_rng();
+                    let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
+                    let dice_event = GameEvent::RollResult {
+                        player_id: self.active_player_id,
+                        dice: store::Dice {
+                            values: dice_values,
+                        },
+                    };
+                    // print!("o");
+                    if self.game.validate(&dice_event) {
+                        self.game.consume(&dice_event);
+                        let (points, adv_points) = self.game.dice_points;
+                        reward += REWARD_RATIO * (points - adv_points) as f32;
+                        if points > 0 {
+                            is_rollpoint = true;
+                            // println!("info: rolled for {reward}");
+                        }
+                        // Récompense proportionnelle aux points
+                    }
+                }
+            } else {
+                // Pénalité pour action invalide
+                // on annule les précédents reward
+                // et on indique une valeur reconnaissable pour statistiques
+                reward = ERROR_REWARD;
+            }
+        }
+
+        (reward, is_rollpoint)
+    }
+
+    /// Fait jouer l'adversaire avec une stratégie simple
+    fn play_opponent_if_needed(&mut self) -> f32 {
+        // print!("z?");
+        let mut reward = 0.0;
+
+        // Si c'est le tour de l'adversaire, jouer automatiquement
+        if self.game.active_player_id == self.opponent_id && self.game.stage != Stage::Ended {
+            // Utiliser la stratégie default pour l'adversaire
+            use crate::BotStrategy;
+
+            let mut strategy = crate::strategy::random::RandomStrategy::default();
+            strategy.set_player_id(self.opponent_id);
+            if let Some(color) = self.game.player_color_by_id(&self.opponent_id) {
+                strategy.set_color(color);
+            }
+            *strategy.get_mut_game() = self.game.clone();
+
+            // Exécuter l'action selon le turn_stage
+            let mut calculate_points = false;
+            let opponent_color = store::Color::Black;
+            let event = match self.game.turn_stage {
+                TurnStage::RollDice => GameEvent::Roll {
+                    player_id: self.opponent_id,
+                },
+                TurnStage::RollWaiting => {
+                    let mut rng = thread_rng();
+                    let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
+                    calculate_points = true; // comment to replicate burnrl_before
+                    GameEvent::RollResult {
+                        player_id: self.opponent_id,
+                        dice: store::Dice {
+                            values: dice_values,
+                        },
+                    }
+                }
+                TurnStage::MarkPoints => {
+                    panic!("in play_opponent_if_needed > TurnStage::MarkPoints");
+                    // let dice_roll_count = self
+                    //     .game
+                    //     .players
+                    //     .get(&self.opponent_id)
+                    //     .unwrap()
+                    //     .dice_roll_count;
+                    // let points_rules =
+                    //     PointsRules::new(&opponent_color, &self.game.board, self.game.dice);
+                    // GameEvent::Mark {
+                    //     player_id: self.opponent_id,
+                    //     points: points_rules.get_points(dice_roll_count).0,
+                    // }
+                }
+                TurnStage::MarkAdvPoints => {
+                    let dice_roll_count = self
+                        .game
+                        .players
+                        .get(&self.opponent_id)
+                        .unwrap()
+                        .dice_roll_count;
+                    let points_rules =
+                        PointsRules::new(&opponent_color, &self.game.board, self.game.dice);
+                    // pas de reward : déjà comptabilisé lors du tour de blanc
+                    GameEvent::Mark {
+                        player_id: self.opponent_id,
+                        points: points_rules.get_points(dice_roll_count).1,
+                    }
+                }
+                TurnStage::HoldOrGoChoice => {
+                    // Stratégie simple : toujours continuer
+                    GameEvent::Go {
+                        player_id: self.opponent_id,
+                    }
+                }
+                TurnStage::Move => GameEvent::Move {
+                    player_id: self.opponent_id,
+                    moves: strategy.choose_move(),
+                },
+            };
+
+            if self.game.validate(&event) {
+                self.game.consume(&event);
+                // print!(".");
+                if calculate_points {
+                    // print!("x");
+                    let dice_roll_count = self
+                        .game
+                        .players
+                        .get(&self.opponent_id)
+                        .unwrap()
+                        .dice_roll_count;
+                    let points_rules =
+                        PointsRules::new(&opponent_color, &self.game.board, self.game.dice);
+                    let (points, adv_points) = points_rules.get_points(dice_roll_count);
+                    // Récompense proportionnelle aux points
+                    let adv_reward = REWARD_RATIO * (points - adv_points) as f32;
+                    reward -= adv_reward;
+                    // if adv_reward != 0.0 {
+                    //     println!("info: opponent : {adv_reward} -> {reward}");
+                    // }
+                }
+            }
+        }
+        reward
+    }
+}
+
+impl AsMut<TrictracEnvironment> for TrictracEnvironment {
+    fn as_mut(&mut self) -> &mut Self {
+        self
+    }
+}

bot/src/burnrl/environment_valid.rs

@@ -1,12 +1,9 @@
-use crate::training_common;
+use crate::training_common_big;
 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 = -1.0012121;
-const REWARD_RATIO: f32 = 0.1;
-
 /// État du jeu Trictrac pour burn-rl
 #[derive(Debug, Clone, Copy)]
 pub struct TrictracState {
@@ -217,16 +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<training_common::TrictracAction> {
+    pub fn convert_action(action: TrictracAction) -> Option<training_common_big::TrictracAction> {
-        training_common::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<training_common::TrictracAction> {
+    ) -> Option<training_common_big::TrictracAction> {
-        use training_common::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);
@@ -243,19 +240,72 @@ impl TrictracEnvironment {
     /// Exécute une action Trictrac dans le jeu
     // fn execute_action(
     //     &mut self,
-    //     action: training_common::TrictracAction,
+    //     action: training_common_big::TrictracAction,
     // ) -> Result<f32, Box<dyn std::error::Error>> {
-    fn execute_action(&mut self, action: training_common::TrictracAction) -> (f32, bool) {
+    fn execute_action(&mut self, action: training_common_big::TrictracAction) -> (f32, bool) {
-        use training_common::TrictracAction;
+        use training_common_big::TrictracAction;
 
         let mut reward = 0.0;
         let mut is_rollpoint = false;
 
+        let event = match action {
+            TrictracAction::Roll => {
+                // Lancer les dés
+                Some(GameEvent::Roll {
+                    player_id: self.active_player_id,
+                })
+            }
+            // TrictracAction::Mark => {
+            //     // Marquer des points
+            //     let points = self.game.
+            //     reward += 0.1 * points as f32;
+            //     Some(GameEvent::Mark {
+            //         player_id: self.active_player_id,
+            //         points,
+            //     })
+            // }
+            TrictracAction::Go => {
+                // Continuer après avoir gagné un trou
+                Some(GameEvent::Go {
+                    player_id: self.active_player_id,
+                })
+            }
+            TrictracAction::Move {
+                dice_order,
+                from1,
+                from2,
+            } => {
+                // Effectuer un mouvement
+                let (dice1, dice2) = if dice_order {
+                    (self.game.dice.values.0, self.game.dice.values.1)
+                } else {
+                    (self.game.dice.values.1, self.game.dice.values.0)
+                };
+                let mut to1 = from1 + dice1 as usize;
+                let mut to2 = from2 + dice2 as usize;
+
+                // Gestion prise de coin par puissance
+                let opp_rest_field = 13;
+                if to1 == opp_rest_field && to2 == opp_rest_field {
+                    to1 -= 1;
+                    to2 -= 1;
+                }
+
+                let checker_move1 = store::CheckerMove::new(from1, to1).unwrap_or_default();
+                let checker_move2 = store::CheckerMove::new(from2, to2).unwrap_or_default();
+
+                Some(GameEvent::Move {
+                    player_id: self.active_player_id,
+                    moves: (checker_move1, checker_move2),
+                })
+            }
+        };
+
         // Appliquer l'événement si valide
-        if let Some(event) = action.to_event(&self.game) {
+        if let Some(event) = event {
             if self.game.validate(&event) {
                 self.game.consume(&event);
-                // reward += REWARD_VALID_MOVE;
+
                 // Simuler le résultat des dés après un Roll
                 if matches!(action, TrictracAction::Roll) {
                     let mut rng = thread_rng();
@@ -269,7 +319,7 @@ impl TrictracEnvironment {
                     if self.game.validate(&dice_event) {
                         self.game.consume(&dice_event);
                         let (points, adv_points) = self.game.dice_points;
-                        reward += REWARD_RATIO * (points as f32 - adv_points as f32);
+                        reward += Self::REWARD_RATIO * (points - adv_points) as f32;
                         if points > 0 {
                             is_rollpoint = true;
                             // println!("info: rolled for {reward}");
@@ -281,12 +331,9 @@ impl TrictracEnvironment {
                 // Pénalité pour action invalide
                 // on annule les précédents reward
                 // et on indique une valeur reconnaissable pour statistiques
-                reward = ERROR_REWARD;
+                println!("info: action invalide -> err_reward");
-                self.game.mark_points_for_bot_training(self.opponent_id, 1);
+                reward = Self::ERROR_REWARD;
             }
-        } else {
-            reward = ERROR_REWARD;
-            self.game.mark_points_for_bot_training(self.opponent_id, 1);
         }
 
         (reward, is_rollpoint)

bot/src/burnrl/main.rs

@@ -1,5 +1,8 @@
-use bot::burnrl::algos::{dqn, dqn_valid, ppo, ppo_valid, sac, sac_valid};
+use bot::burnrl::algos::{
+    dqn, dqn_big, dqn_valid, ppo, ppo_big, ppo_valid, sac, sac_big, sac_valid,
+};
 use bot::burnrl::environment::TrictracEnvironment;
+use bot::burnrl::environment_big::TrictracEnvironment as TrictracEnvironmentBig;
 use bot::burnrl::environment_valid::TrictracEnvironment as TrictracEnvironmentValid;
 use bot::burnrl::utils::{demo_model, Config};
 use burn::backend::{Autodiff, NdArray};
@@ -33,6 +36,16 @@ fn main() {
             println!("> Test avec le modèle chargé");
             demo_model(loaded_agent);
         }
+        "dqn_big" => {
+            let _agent = dqn_big::run::<TrictracEnvironmentBig, Backend>(&conf, false);
+            println!("> Chargement du modèle pour test");
+            let loaded_model = dqn_big::load_model(conf.dense_size, &path);
+            let loaded_agent: burn_rl::agent::DQN<TrictracEnvironmentBig, _, _> =
+                burn_rl::agent::DQN::new(loaded_model.unwrap());
+
+            println!("> Test avec le modèle chargé");
+            demo_model(loaded_agent);
+        }
         "dqn_valid" => {
             let _agent = dqn_valid::run::<TrictracEnvironmentValid, Backend>(&conf, false);
             println!("> Chargement du modèle pour test");
@@ -53,6 +66,16 @@ fn main() {
             println!("> Test avec le modèle chargé");
             demo_model(loaded_agent);
         }
+        "sac_big" => {
+            let _agent = sac_big::run::<TrictracEnvironmentBig, Backend>(&conf, false);
+            println!("> Chargement du modèle pour test");
+            let loaded_model = sac_big::load_model(conf.dense_size, &path);
+            let loaded_agent: burn_rl::agent::SAC<TrictracEnvironmentBig, _, _> =
+                burn_rl::agent::SAC::new(loaded_model.unwrap());
+
+            println!("> Test avec le modèle chargé");
+            demo_model(loaded_agent);
+        }
         "sac_valid" => {
             let _agent = sac_valid::run::<TrictracEnvironmentValid, Backend>(&conf, false);
             println!("> Chargement du modèle pour test");
@@ -73,6 +96,16 @@ fn main() {
             println!("> Test avec le modèle chargé");
             demo_model(loaded_agent);
         }
+        "ppo_big" => {
+            let _agent = ppo_big::run::<TrictracEnvironmentBig, Backend>(&conf, false);
+            println!("> Chargement du modèle pour test");
+            let loaded_model = ppo_big::load_model(conf.dense_size, &path);
+            let loaded_agent: burn_rl::agent::PPO<TrictracEnvironmentBig, _, _> =
+                burn_rl::agent::PPO::new(loaded_model.unwrap());
+
+            println!("> Test avec le modèle chargé");
+            demo_model(loaded_agent);
+        }
         "ppo_valid" => {
             let _agent = ppo_valid::run::<TrictracEnvironmentValid, Backend>(&conf, false);
             println!("> Chargement du modèle pour test");

bot/src/burnrl/mod.rs

@@ -1,4 +1,5 @@
 pub mod algos;
 pub mod environment;
+pub mod environment_big;
 pub mod environment_valid;
 pub mod utils;

bot/src/dqn_simple/dqn_model.rs

@@ -0,0 +1,153 @@
+use crate::training_common_big::TrictracAction;
+use serde::{Deserialize, Serialize};
+
+/// Configuration pour l'agent DQN
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct DqnConfig {
+    pub state_size: usize,
+    pub hidden_size: usize,
+    pub num_actions: usize,
+    pub learning_rate: f64,
+    pub gamma: f64,
+    pub epsilon: f64,
+    pub epsilon_decay: f64,
+    pub epsilon_min: f64,
+    pub replay_buffer_size: usize,
+    pub batch_size: usize,
+}
+
+impl Default for DqnConfig {
+    fn default() -> Self {
+        Self {
+            state_size: 36,
+            hidden_size: 512, // Augmenter la taille pour gérer l'espace d'actions élargi
+            num_actions: TrictracAction::action_space_size(),
+            learning_rate: 0.001,
+            gamma: 0.99,
+            epsilon: 0.1,
+            epsilon_decay: 0.995,
+            epsilon_min: 0.01,
+            replay_buffer_size: 10000,
+            batch_size: 32,
+        }
+    }
+}
+
+/// Réseau de neurones DQN simplifié (matrice de poids basique)
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct SimpleNeuralNetwork {
+    pub weights1: Vec<Vec<f32>>,
+    pub biases1: Vec<f32>,
+    pub weights2: Vec<Vec<f32>>,
+    pub biases2: Vec<f32>,
+    pub weights3: Vec<Vec<f32>>,
+    pub biases3: Vec<f32>,
+}
+
+impl SimpleNeuralNetwork {
+    pub fn new(input_size: usize, hidden_size: usize, output_size: usize) -> Self {
+        use rand::{thread_rng, Rng};
+        let mut rng = thread_rng();
+
+        // Initialisation aléatoire des poids avec Xavier/Glorot
+        let scale1 = (2.0 / input_size as f32).sqrt();
+        let weights1 = (0..hidden_size)
+            .map(|_| {
+                (0..input_size)
+                    .map(|_| rng.gen_range(-scale1..scale1))
+                    .collect()
+            })
+            .collect();
+        let biases1 = vec![0.0; hidden_size];
+
+        let scale2 = (2.0 / hidden_size as f32).sqrt();
+        let weights2 = (0..hidden_size)
+            .map(|_| {
+                (0..hidden_size)
+                    .map(|_| rng.gen_range(-scale2..scale2))
+                    .collect()
+            })
+            .collect();
+        let biases2 = vec![0.0; hidden_size];
+
+        let scale3 = (2.0 / hidden_size as f32).sqrt();
+        let weights3 = (0..output_size)
+            .map(|_| {
+                (0..hidden_size)
+                    .map(|_| rng.gen_range(-scale3..scale3))
+                    .collect()
+            })
+            .collect();
+        let biases3 = vec![0.0; output_size];
+
+        Self {
+            weights1,
+            biases1,
+            weights2,
+            biases2,
+            weights3,
+            biases3,
+        }
+    }
+
+    pub fn forward(&self, input: &[f32]) -> Vec<f32> {
+        // Première couche
+        let mut layer1: Vec<f32> = self.biases1.clone();
+        for (i, neuron_weights) in self.weights1.iter().enumerate() {
+            for (j, &weight) in neuron_weights.iter().enumerate() {
+                if j < input.len() {
+                    layer1[i] += input[j] * weight;
+                }
+            }
+            layer1[i] = layer1[i].max(0.0); // ReLU
+        }
+
+        // Deuxième couche
+        let mut layer2: Vec<f32> = self.biases2.clone();
+        for (i, neuron_weights) in self.weights2.iter().enumerate() {
+            for (j, &weight) in neuron_weights.iter().enumerate() {
+                if j < layer1.len() {
+                    layer2[i] += layer1[j] * weight;
+                }
+            }
+            layer2[i] = layer2[i].max(0.0); // ReLU
+        }
+
+        // Couche de sortie
+        let mut output: Vec<f32> = self.biases3.clone();
+        for (i, neuron_weights) in self.weights3.iter().enumerate() {
+            for (j, &weight) in neuron_weights.iter().enumerate() {
+                if j < layer2.len() {
+                    output[i] += layer2[j] * weight;
+                }
+            }
+        }
+
+        output
+    }
+
+    pub fn get_best_action(&self, input: &[f32]) -> usize {
+        let q_values = self.forward(input);
+        q_values
+            .iter()
+            .enumerate()
+            .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())
+            .map(|(index, _)| index)
+            .unwrap_or(0)
+    }
+
+    pub fn save<P: AsRef<std::path::Path>>(
+        &self,
+        path: P,
+    ) -> Result<(), Box<dyn std::error::Error>> {
+        let data = serde_json::to_string_pretty(self)?;
+        std::fs::write(path, data)?;
+        Ok(())
+    }
+
+    pub fn load<P: AsRef<std::path::Path>>(path: P) -> Result<Self, Box<dyn std::error::Error>> {
+        let data = std::fs::read_to_string(path)?;
+        let network = serde_json::from_str(&data)?;
+        Ok(network)
+    }
+}

bot/src/dqn_simple/dqn_trainer.rs

@@ -0,0 +1,494 @@
+use crate::{CheckerMove, Color, GameState, PlayerId};
+use rand::prelude::SliceRandom;
+use rand::{thread_rng, Rng};
+use serde::{Deserialize, Serialize};
+use std::collections::VecDeque;
+use store::{GameEvent, MoveRules, PointsRules, Stage, TurnStage};
+
+use super::dqn_model::{DqnConfig, SimpleNeuralNetwork};
+use crate::training_common_big::{get_valid_actions, TrictracAction};
+
+/// Expérience pour le buffer de replay
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Experience {
+    pub state: Vec<f32>,
+    pub action: TrictracAction,
+    pub reward: f32,
+    pub next_state: Vec<f32>,
+    pub done: bool,
+}
+
+/// Buffer de replay pour stocker les expériences
+#[derive(Debug)]
+pub struct ReplayBuffer {
+    buffer: VecDeque<Experience>,
+    capacity: usize,
+}
+
+impl ReplayBuffer {
+    pub fn new(capacity: usize) -> Self {
+        Self {
+            buffer: VecDeque::with_capacity(capacity),
+            capacity,
+        }
+    }
+
+    pub fn push(&mut self, experience: Experience) {
+        if self.buffer.len() >= self.capacity {
+            self.buffer.pop_front();
+        }
+        self.buffer.push_back(experience);
+    }
+
+    pub fn sample(&self, batch_size: usize) -> Vec<Experience> {
+        let mut rng = thread_rng();
+        let len = self.buffer.len();
+        if len < batch_size {
+            return self.buffer.iter().cloned().collect();
+        }
+
+        let mut batch = Vec::with_capacity(batch_size);
+        for _ in 0..batch_size {
+            let idx = rng.gen_range(0..len);
+            batch.push(self.buffer[idx].clone());
+        }
+        batch
+    }
+
+    pub fn is_empty(&self) -> bool {
+        self.buffer.is_empty()
+    }
+
+    pub fn len(&self) -> usize {
+        self.buffer.len()
+    }
+}
+
+/// Agent DQN pour l'apprentissage par renforcement
+#[derive(Debug)]
+pub struct DqnAgent {
+    config: DqnConfig,
+    model: SimpleNeuralNetwork,
+    target_model: SimpleNeuralNetwork,
+    replay_buffer: ReplayBuffer,
+    epsilon: f64,
+    step_count: usize,
+}
+
+impl DqnAgent {
+    pub fn new(config: DqnConfig) -> Self {
+        let model =
+            SimpleNeuralNetwork::new(config.state_size, config.hidden_size, config.num_actions);
+        let target_model = model.clone();
+        let replay_buffer = ReplayBuffer::new(config.replay_buffer_size);
+        let epsilon = config.epsilon;
+
+        Self {
+            config,
+            model,
+            target_model,
+            replay_buffer,
+            epsilon,
+            step_count: 0,
+        }
+    }
+
+    pub fn select_action(&mut self, game_state: &GameState, state: &[f32]) -> TrictracAction {
+        let valid_actions = get_valid_actions(game_state);
+
+        if valid_actions.is_empty() {
+            // Fallback si aucune action valide
+            return TrictracAction::Roll;
+        }
+
+        let mut rng = thread_rng();
+        if rng.gen::<f64>() < self.epsilon {
+            // Exploration : action valide aléatoire
+            valid_actions
+                .choose(&mut rng)
+                .cloned()
+                .unwrap_or(TrictracAction::Roll)
+        } else {
+            // Exploitation : meilleure action valide selon le modèle
+            let q_values = self.model.forward(state);
+
+            let mut best_action = &valid_actions[0];
+            let mut best_q_value = f32::NEG_INFINITY;
+
+            for action in &valid_actions {
+                let action_index = action.to_action_index();
+                if action_index < q_values.len() {
+                    let q_value = q_values[action_index];
+                    if q_value > best_q_value {
+                        best_q_value = q_value;
+                        best_action = action;
+                    }
+                }
+            }
+
+            best_action.clone()
+        }
+    }
+
+    pub fn store_experience(&mut self, experience: Experience) {
+        self.replay_buffer.push(experience);
+    }
+
+    pub fn train(&mut self) {
+        if self.replay_buffer.len() < self.config.batch_size {
+            return;
+        }
+
+        // Pour l'instant, on simule l'entraînement en mettant à jour epsilon
+        // Dans une implémentation complète, ici on ferait la backpropagation
+        self.epsilon = (self.epsilon * self.config.epsilon_decay).max(self.config.epsilon_min);
+        self.step_count += 1;
+
+        // Mise à jour du target model tous les 100 steps
+        if self.step_count % 100 == 0 {
+            self.target_model = self.model.clone();
+        }
+    }
+
+    pub fn save_model<P: AsRef<std::path::Path>>(
+        &self,
+        path: P,
+    ) -> Result<(), Box<dyn std::error::Error>> {
+        self.model.save(path)
+    }
+
+    pub fn get_epsilon(&self) -> f64 {
+        self.epsilon
+    }
+
+    pub fn get_step_count(&self) -> usize {
+        self.step_count
+    }
+}
+
+/// Environnement Trictrac pour l'entraînement
+#[derive(Debug)]
+pub struct TrictracEnv {
+    pub game_state: GameState,
+    pub agent_player_id: PlayerId,
+    pub opponent_player_id: PlayerId,
+    pub agent_color: Color,
+    pub max_steps: usize,
+    pub current_step: usize,
+}
+
+impl Default for TrictracEnv {
+    fn default() -> Self {
+        let mut game_state = GameState::new(false);
+        game_state.init_player("agent");
+        game_state.init_player("opponent");
+
+        Self {
+            game_state,
+            agent_player_id: 1,
+            opponent_player_id: 2,
+            agent_color: Color::White,
+            max_steps: 1000,
+            current_step: 0,
+        }
+    }
+}
+
+impl TrictracEnv {
+    pub fn reset(&mut self) -> Vec<f32> {
+        self.game_state = GameState::new(false);
+        self.game_state.init_player("agent");
+        self.game_state.init_player("opponent");
+
+        // Commencer la partie
+        self.game_state.consume(&GameEvent::BeginGame {
+            goes_first: self.agent_player_id,
+        });
+
+        self.current_step = 0;
+        self.game_state.to_vec_float()
+    }
+
+    pub fn step(&mut self, action: TrictracAction) -> (Vec<f32>, f32, bool) {
+        let mut reward = 0.0;
+
+        // Appliquer l'action de l'agent
+        if self.game_state.active_player_id == self.agent_player_id {
+            reward += self.apply_agent_action(action);
+        }
+
+        // Faire jouer l'adversaire (stratégie simple)
+        while self.game_state.active_player_id == self.opponent_player_id
+            && self.game_state.stage != Stage::Ended
+        {
+            reward += self.play_opponent_turn();
+        }
+
+        // Vérifier si la partie est terminée
+        let done = self.game_state.stage == Stage::Ended
+            || self.game_state.determine_winner().is_some()
+            || self.current_step >= self.max_steps;
+
+        // Récompense finale si la partie est terminée
+        if done {
+            if let Some(winner) = self.game_state.determine_winner() {
+                if winner == self.agent_player_id {
+                    reward += 100.0; // Bonus pour gagner
+                } else {
+                    reward -= 50.0; // Pénalité pour perdre
+                }
+            }
+        }
+
+        self.current_step += 1;
+        let next_state = self.game_state.to_vec_float();
+        (next_state, reward, done)
+    }
+
+    fn apply_agent_action(&mut self, action: TrictracAction) -> f32 {
+        let mut reward = 0.0;
+
+        let event = match action {
+            TrictracAction::Roll => {
+                // Lancer les dés
+                reward += 0.1;
+                Some(GameEvent::Roll {
+                    player_id: self.agent_player_id,
+                })
+            }
+            // TrictracAction::Mark => {
+            //     // Marquer des points
+            //     let points = self.game_state.
+            //     reward += 0.1 * points as f32;
+            //     Some(GameEvent::Mark {
+            //         player_id: self.agent_player_id,
+            //         points,
+            //     })
+            // }
+            TrictracAction::Go => {
+                // Continuer après avoir gagné un trou
+                reward += 0.2;
+                Some(GameEvent::Go {
+                    player_id: self.agent_player_id,
+                })
+            }
+            TrictracAction::Move {
+                dice_order,
+                from1,
+                from2,
+            } => {
+                // Effectuer un mouvement
+                let (dice1, dice2) = if dice_order {
+                    (self.game_state.dice.values.0, self.game_state.dice.values.1)
+                } else {
+                    (self.game_state.dice.values.1, self.game_state.dice.values.0)
+                };
+                let mut to1 = from1 + dice1 as usize;
+                let mut to2 = from2 + dice2 as usize;
+
+                // Gestion prise de coin par puissance
+                let opp_rest_field = 13;
+                if to1 == opp_rest_field && to2 == opp_rest_field {
+                    to1 -= 1;
+                    to2 -= 1;
+                }
+
+                let checker_move1 = store::CheckerMove::new(from1, to1).unwrap_or_default();
+                let checker_move2 = store::CheckerMove::new(from2, to2).unwrap_or_default();
+
+                reward += 0.2;
+                Some(GameEvent::Move {
+                    player_id: self.agent_player_id,
+                    moves: (checker_move1, checker_move2),
+                })
+            }
+        };
+
+        // Appliquer l'événement si valide
+        if let Some(event) = event {
+            if self.game_state.validate(&event) {
+                self.game_state.consume(&event);
+
+                // Simuler le résultat des dés après un Roll
+                if matches!(action, TrictracAction::Roll) {
+                    let mut rng = thread_rng();
+                    let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
+                    let dice_event = GameEvent::RollResult {
+                        player_id: self.agent_player_id,
+                        dice: store::Dice {
+                            values: dice_values,
+                        },
+                    };
+                    if self.game_state.validate(&dice_event) {
+                        self.game_state.consume(&dice_event);
+                    }
+                }
+            } else {
+                // Pénalité pour action invalide
+                reward -= 2.0;
+            }
+        }
+
+        reward
+    }
+
+    // TODO : use default bot strategy
+    fn play_opponent_turn(&mut self) -> f32 {
+        let mut reward = 0.0;
+        let event = match self.game_state.turn_stage {
+            TurnStage::RollDice => GameEvent::Roll {
+                player_id: self.opponent_player_id,
+            },
+            TurnStage::RollWaiting => {
+                let mut rng = thread_rng();
+                let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
+                GameEvent::RollResult {
+                    player_id: self.opponent_player_id,
+                    dice: store::Dice {
+                        values: dice_values,
+                    },
+                }
+            }
+            TurnStage::MarkAdvPoints | TurnStage::MarkPoints => {
+                let opponent_color = self.agent_color.opponent_color();
+                let dice_roll_count = self
+                    .game_state
+                    .players
+                    .get(&self.opponent_player_id)
+                    .unwrap()
+                    .dice_roll_count;
+                let points_rules = PointsRules::new(
+                    &opponent_color,
+                    &self.game_state.board,
+                    self.game_state.dice,
+                );
+                let (points, adv_points) = points_rules.get_points(dice_roll_count);
+                reward -= 0.3 * (points - adv_points) as f32; // Récompense proportionnelle aux points
+
+                GameEvent::Mark {
+                    player_id: self.opponent_player_id,
+                    points,
+                }
+            }
+            TurnStage::Move => {
+                let opponent_color = self.agent_color.opponent_color();
+                let rules = MoveRules::new(
+                    &opponent_color,
+                    &self.game_state.board,
+                    self.game_state.dice,
+                );
+                let possible_moves = rules.get_possible_moves_sequences(true, vec![]);
+
+                // Stratégie simple : choix aléatoire
+                let mut rng = thread_rng();
+                let choosen_move = *possible_moves
+                    .choose(&mut rng)
+                    .unwrap_or(&(CheckerMove::default(), CheckerMove::default()));
+
+                GameEvent::Move {
+                    player_id: self.opponent_player_id,
+                    moves: if opponent_color == Color::White {
+                        choosen_move
+                    } else {
+                        (choosen_move.0.mirror(), choosen_move.1.mirror())
+                    },
+                }
+            }
+            TurnStage::HoldOrGoChoice => {
+                // Stratégie simple : toujours continuer
+                GameEvent::Go {
+                    player_id: self.opponent_player_id,
+                }
+            }
+        };
+        if self.game_state.validate(&event) {
+            self.game_state.consume(&event);
+        }
+        reward
+    }
+}
+
+/// Entraîneur pour le modèle DQN
+pub struct DqnTrainer {
+    agent: DqnAgent,
+    env: TrictracEnv,
+}
+
+impl DqnTrainer {
+    pub fn new(config: DqnConfig) -> Self {
+        Self {
+            agent: DqnAgent::new(config),
+            env: TrictracEnv::default(),
+        }
+    }
+
+    pub fn train_episode(&mut self) -> f32 {
+        let mut total_reward = 0.0;
+        let mut state = self.env.reset();
+        // let mut step_count = 0;
+
+        loop {
+            // step_count += 1;
+            let action = self.agent.select_action(&self.env.game_state, &state);
+            let (next_state, reward, done) = self.env.step(action.clone());
+            total_reward += reward;
+
+            let experience = Experience {
+                state: state.clone(),
+                action,
+                reward,
+                next_state: next_state.clone(),
+                done,
+            };
+            self.agent.store_experience(experience);
+            self.agent.train();
+
+            if done {
+                break;
+            }
+            // if step_count % 100 == 0 {
+            //     println!("{:?}", next_state);
+            // }
+            state = next_state;
+        }
+
+        total_reward
+    }
+
+    pub fn train(
+        &mut self,
+        episodes: usize,
+        save_every: usize,
+        model_path: &str,
+    ) -> Result<(), Box<dyn std::error::Error>> {
+        println!("Démarrage de l'entraînement DQN pour {episodes} épisodes");
+
+        for episode in 1..=episodes {
+            let reward = self.train_episode();
+
+            if episode % 100 == 0 {
+                println!(
+                    "Épisode {}/{}: Récompense = {:.2}, Epsilon = {:.3}, Steps = {}",
+                    episode,
+                    episodes,
+                    reward,
+                    self.agent.get_epsilon(),
+                    self.agent.get_step_count()
+                );
+            }
+
+            if episode % save_every == 0 {
+                let save_path = format!("{model_path}_episode_{episode}.json");
+                self.agent.save_model(&save_path)?;
+                println!("Modèle sauvegardé : {save_path}");
+            }
+        }
+
+        // Sauvegarder le modèle final
+        let final_path = format!("{model_path}_final.json");
+        self.agent.save_model(&final_path)?;
+        println!("Modèle final sauvegardé : {final_path}");
+
+        Ok(())
+    }
+}

bot/src/dqn_simple/main.rs

@@ -0,0 +1,109 @@
+use bot::dqn_simple::dqn_model::DqnConfig;
+use bot::dqn_simple::dqn_trainer::DqnTrainer;
+use bot::training_common::TrictracAction;
+use std::env;
+
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+    env_logger::init();
+
+    let args: Vec<String> = env::args().collect();
+
+    // Paramètres par défaut
+    let mut episodes = 1000;
+    let mut model_path = "models/dqn_model".to_string();
+    let mut save_every = 100;
+
+    // Parser les arguments de ligne de commande
+    let mut i = 1;
+    while i < args.len() {
+        match args[i].as_str() {
+            "--episodes" => {
+                if i + 1 < args.len() {
+                    episodes = args[i + 1].parse().unwrap_or(1000);
+                    i += 2;
+                } else {
+                    eprintln!("Erreur : --episodes nécessite une valeur");
+                    std::process::exit(1);
+                }
+            }
+            "--model-path" => {
+                if i + 1 < args.len() {
+                    model_path = args[i + 1].clone();
+                    i += 2;
+                } else {
+                    eprintln!("Erreur : --model-path nécessite une valeur");
+                    std::process::exit(1);
+                }
+            }
+            "--save-every" => {
+                if i + 1 < args.len() {
+                    save_every = args[i + 1].parse().unwrap_or(100);
+                    i += 2;
+                } else {
+                    eprintln!("Erreur : --save-every nécessite une valeur");
+                    std::process::exit(1);
+                }
+            }
+            "--help" | "-h" => {
+                print_help();
+                std::process::exit(0);
+            }
+            _ => {
+                eprintln!("Argument inconnu : {}", args[i]);
+                print_help();
+                std::process::exit(1);
+            }
+        }
+    }
+
+    // Créer le dossier models s'il n'existe pas
+    std::fs::create_dir_all("models")?;
+
+    println!("Configuration d'entraînement DQN :");
+    println!("  Épisodes : {episodes}");
+    println!("  Chemin du modèle : {model_path}");
+    println!("  Sauvegarde tous les {save_every} épisodes");
+    println!();
+
+    // Configuration DQN
+    let config = DqnConfig {
+        state_size: 36, // state.to_vec size
+        hidden_size: 256,
+        num_actions: TrictracAction::action_space_size(),
+        learning_rate: 0.001,
+        gamma: 0.99,
+        epsilon: 0.9, // Commencer avec plus d'exploration
+        epsilon_decay: 0.995,
+        epsilon_min: 0.01,
+        replay_buffer_size: 10000,
+        batch_size: 32,
+    };
+
+    // Créer et lancer l'entraîneur
+    let mut trainer = DqnTrainer::new(config);
+    trainer.train(episodes, save_every, &model_path)?;
+
+    println!("Entraînement terminé avec succès !");
+    println!("Pour utiliser le modèle entraîné :");
+    println!("  cargo run --bin=client_cli -- --bot dqn:{model_path}_final.json,dummy");
+
+    Ok(())
+}
+
+fn print_help() {
+    println!("Entraîneur DQN pour Trictrac");
+    println!();
+    println!("USAGE:");
+    println!("  cargo run --bin=train_dqn [OPTIONS]");
+    println!();
+    println!("OPTIONS:");
+    println!("  --episodes <NUM>      Nombre d'épisodes d'entraînement (défaut: 1000)");
+    println!("  --model-path <PATH>   Chemin de base pour sauvegarder les modèles (défaut: models/dqn_model)");
+    println!("  --save-every <NUM>    Sauvegarder le modèle tous les N épisodes (défaut: 100)");
+    println!("  -h, --help            Afficher cette aide");
+    println!();
+    println!("EXEMPLES:");
+    println!("  cargo run --bin=train_dqn");
+    println!("  cargo run --bin=train_dqn -- --episodes 5000 --save-every 500");
+    println!("  cargo run --bin=train_dqn -- --model-path models/my_model --episodes 2000");
+}

bot/src/dqn_simple/mod.rs

@@ -0,0 +1,2 @@
+pub mod dqn_model;
+pub mod dqn_trainer;

bot/src/lib.rs

@@ -1,11 +1,14 @@
 pub mod burnrl;
+pub mod dqn_simple;
 pub mod strategy;
 pub mod training_common;
+pub mod training_common_big;
 pub mod trictrac_board;
 
 use log::debug;
 use store::{CheckerMove, Color, GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage};
 pub use strategy::default::DefaultStrategy;
+pub use strategy::dqn::DqnStrategy;
 pub use strategy::dqnburn::DqnBurnStrategy;
 pub use strategy::erroneous_moves::ErroneousStrategy;
 pub use strategy::random::RandomStrategy;

bot/src/strategy/dqn.rs

@@ -0,0 +1,174 @@
+use crate::{BotStrategy, CheckerMove, Color, GameState, PlayerId};
+use log::info;
+use std::path::Path;
+use store::MoveRules;
+
+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)]
+pub struct DqnStrategy {
+    pub game: GameState,
+    pub player_id: PlayerId,
+    pub color: Color,
+    pub model: Option<SimpleNeuralNetwork>,
+}
+
+impl Default for DqnStrategy {
+    fn default() -> Self {
+        Self {
+            game: GameState::default(),
+            player_id: 1,
+            color: Color::White,
+            model: None,
+        }
+    }
+}
+
+impl DqnStrategy {
+    pub fn new() -> Self {
+        Self::default()
+    }
+
+    pub fn new_with_model<P: AsRef<Path> + std::fmt::Debug>(model_path: P) -> Self {
+        let mut strategy = Self::new();
+        if let Ok(model) = SimpleNeuralNetwork::load(&model_path) {
+            info!("Loading model {model_path:?}");
+            strategy.model = Some(model);
+        }
+        strategy
+    }
+
+    /// Utilise le modèle DQN pour choisir une action valide
+    fn get_dqn_action(&self) -> Option<TrictracAction> {
+        if let Some(ref model) = self.model {
+            let state = self.game.to_vec_float();
+            let valid_actions = get_valid_actions(&self.game);
+
+            if valid_actions.is_empty() {
+                return None;
+            }
+
+            // Obtenir les Q-values pour toutes les actions
+            let q_values = model.forward(&state);
+
+            // Trouver la meilleure action valide
+            let mut best_action = &valid_actions[0];
+            let mut best_q_value = f32::NEG_INFINITY;
+
+            for action in &valid_actions {
+                let action_index = action.to_action_index();
+                if action_index < q_values.len() {
+                    let q_value = q_values[action_index];
+                    if q_value > best_q_value {
+                        best_q_value = q_value;
+                        best_action = action;
+                    }
+                }
+            }
+
+            Some(best_action.clone())
+        } else {
+            // Fallback : action aléatoire valide
+            sample_valid_action(&self.game)
+        }
+    }
+}
+
+impl BotStrategy for DqnStrategy {
+    fn get_game(&self) -> &GameState {
+        &self.game
+    }
+
+    fn get_mut_game(&mut self) -> &mut GameState {
+        &mut self.game
+    }
+
+    fn set_color(&mut self, color: Color) {
+        self.color = color;
+    }
+
+    fn set_player_id(&mut self, player_id: PlayerId) {
+        self.player_id = player_id;
+    }
+
+    fn calculate_points(&self) -> u8 {
+        self.game.dice_points.0
+    }
+
+    fn calculate_adv_points(&self) -> u8 {
+        self.game.dice_points.1
+    }
+
+    fn choose_go(&self) -> bool {
+        // Utiliser le DQN pour décider si on continue
+        if let Some(action) = self.get_dqn_action() {
+            matches!(action, TrictracAction::Go)
+        } else {
+            // Fallback : toujours continuer
+            true
+        }
+    }
+
+    fn choose_move(&self) -> (CheckerMove, CheckerMove) {
+        // Utiliser le DQN pour choisir le mouvement
+        if let Some(TrictracAction::Move {
+            dice_order,
+            from1,
+            from2,
+        }) = self.get_dqn_action()
+        {
+            let dicevals = self.game.dice.values;
+            let (mut dice1, mut dice2) = if dice_order {
+                (dicevals.0, dicevals.1)
+            } else {
+                (dicevals.1, dicevals.0)
+            };
+
+            if from1 == 0 {
+                // empty move
+                dice1 = 0;
+            }
+            let mut to1 = from1 + dice1 as usize;
+            if 24 < to1 {
+                // sortie
+                to1 = 0;
+            }
+            if from2 == 0 {
+                // empty move
+                dice2 = 0;
+            }
+            let mut to2 = from2 + dice2 as usize;
+            if 24 < to2 {
+                // sortie
+                to2 = 0;
+            }
+
+            let checker_move1 = CheckerMove::new(from1, to1).unwrap_or_default();
+            let checker_move2 = CheckerMove::new(from2, to2).unwrap_or_default();
+
+            let chosen_move = if self.color == Color::White {
+                (checker_move1, checker_move2)
+            } else {
+                (checker_move1.mirror(), checker_move2.mirror())
+            };
+
+            return chosen_move;
+        }
+
+        // Fallback : utiliser la stratégie par défaut
+        let rules = MoveRules::new(&self.color, &self.game.board, self.game.dice);
+        let possible_moves = rules.get_possible_moves_sequences(true, vec![]);
+
+        let chosen_move = *possible_moves
+            .first()
+            .unwrap_or(&(CheckerMove::default(), CheckerMove::default()));
+
+        if self.color == Color::White {
+            chosen_move
+        } else {
+            (chosen_move.0.mirror(), chosen_move.1.mirror())
+        }
+    }
+}

bot/src/strategy/mod.rs

@@ -1,5 +1,6 @@
 pub mod client;
 pub mod default;
+pub mod dqn;
 pub mod dqnburn;
 pub mod erroneous_moves;
 pub mod random;

bot/src/training_common.rs

@@ -1,5 +1,3 @@
-/// training_common.rs : environnement avec espace d'actions optimisé
-/// (514 au lieu de 1252 pour training_common_big.rs de la branche 'big_and_full' )
 use std::cmp::{max, min};
 use std::fmt::{Debug, Display, Formatter};
 

bot/src/training_common_big.rs

@@ -0,0 +1,266 @@
+use std::cmp::{max, min};
+
+use serde::{Deserialize, Serialize};
+use store::{CheckerMove, Dice};
+
+/// Types d'actions possibles dans le jeu
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub enum TrictracAction {
+    /// Lancer les dés
+    Roll,
+    /// Continuer après avoir gagné un trou
+    Go,
+    /// Effectuer un mouvement de pions
+    Move {
+        dice_order: bool, // true = utiliser dice[0] en premier, false = dice[1] en premier
+        from1: usize,     // position de départ du premier pion (0-24)
+        from2: usize,     // position de départ du deuxième pion (0-24)
+    },
+    // Marquer les points : à activer si support des écoles
+    // Mark,
+}
+
+impl TrictracAction {
+    /// Encode une action en index pour le réseau de neurones
+    pub fn to_action_index(&self) -> usize {
+        match self {
+            TrictracAction::Roll => 0,
+            TrictracAction::Go => 1,
+            TrictracAction::Move {
+                dice_order,
+                from1,
+                from2,
+            } => {
+                // Encoder les mouvements dans l'espace d'actions
+                // Indices 2+ pour les mouvements
+                // de 2 à 1251 (2 à  626 pour dé 1 en premier, 627 à 1251 pour dé 2 en premier)
+                let mut start = 2;
+                if !dice_order {
+                    // 25 * 25 = 625
+                    start += 625;
+                }
+                start + from1 * 25 + from2
+            } // TrictracAction::Mark => 1252,
+        }
+    }
+
+    /// Décode un index d'action en TrictracAction
+    pub fn from_action_index(index: usize) -> Option<TrictracAction> {
+        match index {
+            0 => Some(TrictracAction::Roll),
+            // 1252 => Some(TrictracAction::Mark),
+            1 => Some(TrictracAction::Go),
+            i if i >= 3 => {
+                let move_code = i - 3;
+                let (dice_order, from1, from2) = Self::decode_move(move_code);
+                Some(TrictracAction::Move {
+                    dice_order,
+                    from1,
+                    from2,
+                })
+            }
+            _ => None,
+        }
+    }
+
+    /// Décode un entier en paire de mouvements
+    fn decode_move(code: usize) -> (bool, usize, usize) {
+        let mut encoded = code;
+        let dice_order = code < 626;
+        if !dice_order {
+            encoded -= 625
+        }
+        let from1 = encoded / 25;
+        let from2 = 1 + encoded % 25;
+        (dice_order, from1, from2)
+    }
+
+    /// Retourne la taille de l'espace d'actions total
+    pub fn action_space_size() -> usize {
+        // 1 (Roll) + 1 (Go) + mouvements possibles
+        // Pour les mouvements : 2*25*25 = 1250 (choix du dé + position 0-24 pour chaque from)
+        // Mais on peut optimiser en limitant aux positions valides (1-24)
+        2 + (2 * 25 * 25) // = 1252
+    }
+
+    // pub fn to_game_event(&self, player_id: PlayerId, dice: Dice) -> GameEvent {
+    //     match action {
+    //         TrictracAction::Roll => Some(GameEvent::Roll { player_id }),
+    //         TrictracAction::Mark => Some(GameEvent::Mark { player_id, points }),
+    //         TrictracAction::Go => Some(GameEvent::Go { player_id }),
+    //         TrictracAction::Move {
+    //             dice_order,
+    //             from1,
+    //             from2,
+    //         } => {
+    //             // Effectuer un mouvement
+    //             let checker_move1 = store::CheckerMove::new(move1.0, move1.1).unwrap_or_default();
+    //             let checker_move2 = store::CheckerMove::new(move2.0, move2.1).unwrap_or_default();
+    //
+    //             Some(GameEvent::Move {
+    //                 player_id: self.agent_player_id,
+    //                 moves: (checker_move1, checker_move2),
+    //             })
+    //         }
+    //     };
+    // }
+}
+
+/// Obtient les actions valides pour l'état de jeu actuel
+pub fn get_valid_actions(game_state: &crate::GameState) -> Vec<TrictracAction> {
+    use store::TurnStage;
+
+    let mut valid_actions = Vec::new();
+
+    let active_player_id = game_state.active_player_id;
+    let player_color = game_state.player_color_by_id(&active_player_id);
+
+    if let Some(color) = player_color {
+        match game_state.turn_stage {
+            TurnStage::RollDice => {
+                valid_actions.push(TrictracAction::Roll);
+            }
+            TurnStage::MarkPoints | TurnStage::MarkAdvPoints | TurnStage::RollWaiting => {
+                panic!(
+                    "get_valid_actions not implemented for turn stage {:?}",
+                    game_state.turn_stage
+                );
+                // valid_actions.push(TrictracAction::Mark);
+            }
+            TurnStage::HoldOrGoChoice => {
+                valid_actions.push(TrictracAction::Go);
+
+                // Ajoute aussi les mouvements possibles
+                let rules = store::MoveRules::new(&color, &game_state.board, game_state.dice);
+                let possible_moves = rules.get_possible_moves_sequences(true, vec![]);
+
+                // Modififier checker_moves_to_trictrac_action si on doit gérer Black
+                assert_eq!(color, store::Color::White);
+                for (move1, move2) in possible_moves {
+                    valid_actions.push(checker_moves_to_trictrac_action(
+                        &move1,
+                        &move2,
+                        &game_state.dice,
+                    ));
+                }
+            }
+            TurnStage::Move => {
+                let rules = store::MoveRules::new(&color, &game_state.board, game_state.dice);
+                let mut possible_moves = rules.get_possible_moves_sequences(true, vec![]);
+                if possible_moves.is_empty() {
+                    // Empty move
+                    possible_moves.push((CheckerMove::default(), CheckerMove::default()));
+                }
+
+                // Modififier checker_moves_to_trictrac_action si on doit gérer Black
+                assert_eq!(color, store::Color::White);
+                for (move1, move2) in possible_moves {
+                    valid_actions.push(checker_moves_to_trictrac_action(
+                        &move1,
+                        &move2,
+                        &game_state.dice,
+                    ));
+                }
+            }
+        }
+    }
+
+    if valid_actions.is_empty() {
+        panic!("empty valid_actions for state {game_state}");
+    }
+    valid_actions
+}
+
+// Valid only for White player
+fn checker_moves_to_trictrac_action(
+    move1: &CheckerMove,
+    move2: &CheckerMove,
+    dice: &Dice,
+) -> TrictracAction {
+    let to1 = move1.get_to();
+    let to2 = move2.get_to();
+    let from1 = move1.get_from();
+    let from2 = move2.get_from();
+
+    let mut diff_move1 = if to1 > 0 {
+        // Mouvement sans sortie
+        to1 - from1
+    } else {
+        // sortie, on utilise la valeur du dé
+        if to2 > 0 {
+            // sortie pour le mouvement 1 uniquement
+            let dice2 = to2 - from2;
+            if dice2 == dice.values.0 as usize {
+                dice.values.1 as usize
+            } else {
+                dice.values.0 as usize
+            }
+        } else {
+            // double sortie
+            if from1 < from2 {
+                max(dice.values.0, dice.values.1) as usize
+            } else {
+                min(dice.values.0, dice.values.1) as usize
+            }
+        }
+    };
+
+    // modification de diff_move1 si on est dans le cas d'un mouvement par puissance
+    let rest_field = 12;
+    if to1 == rest_field
+        && to2 == rest_field
+        && max(dice.values.0 as usize, dice.values.1 as usize) + min(from1, from2) != rest_field
+    {
+        // prise par puissance
+        diff_move1 += 1;
+    }
+    TrictracAction::Move {
+        dice_order: diff_move1 == dice.values.0 as usize,
+        from1: move1.get_from(),
+        from2: move2.get_from(),
+    }
+}
+
+/// Retourne les indices des actions valides
+pub fn get_valid_action_indices(game_state: &crate::GameState) -> Vec<usize> {
+    get_valid_actions(game_state)
+        .into_iter()
+        .map(|action| action.to_action_index())
+        .collect()
+}
+
+/// Sélectionne une action valide aléatoire
+pub fn sample_valid_action(game_state: &crate::GameState) -> Option<TrictracAction> {
+    use rand::{seq::SliceRandom, thread_rng};
+
+    let valid_actions = get_valid_actions(game_state);
+    let mut rng = thread_rng();
+    valid_actions.choose(&mut rng).cloned()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn to_action_index() {
+        let action = TrictracAction::Move {
+            dice_order: true,
+            from1: 3,
+            from2: 4,
+        };
+        let index = action.to_action_index();
+        assert_eq!(Some(action), TrictracAction::from_action_index(index));
+        assert_eq!(81, index);
+    }
+
+    #[test]
+    fn from_action_index() {
+        let action = TrictracAction::Move {
+            dice_order: true,
+            from1: 3,
+            from2: 4,
+        };
+        assert_eq!(Some(action), TrictracAction::from_action_index(81));
+    }
+}

client_bevy/.cargo/config.toml

@@ -0,0 +1,8 @@
+[target.x86_64-unknown-linux-gnu]
+linker = "clang"
+rustflags = ["-Clink-arg=-fuse-ld=lld", "-Zshare-generics=y"]
+
+# Optional: Uncommenting the following improves compile times, but reduces the amount of debug info to 'line number tables only'
+# In most cases the gains are negligible, but if you are on macos and have slow compile times you should see significant gains.
+#[profile.dev]
+#debug = 1

client_bevy/Cargo.toml

@@ -0,0 +1,14 @@
+[package]
+name = "trictrac-client"
+version = "0.1.0"
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+anyhow = "1.0.75"
+bevy = { version = "0.11.3" }
+bevy_renet = "0.0.9"
+bincode = "1.3.3"
+renet = "0.0.13"
+store = { path = "../store" }

client_bevy/src/main.rs

@@ -0,0 +1,334 @@
+use std::{net::UdpSocket, time::SystemTime};
+
+use renet::transport::{NetcodeClientTransport, NetcodeTransportError, NETCODE_USER_DATA_BYTES};
+use store::{GameEvent, GameState, CheckerMove};
+
+use bevy::prelude::*;
+use bevy::window::PrimaryWindow;
+use bevy_renet::{
+    renet::{transport::ClientAuthentication, ConnectionConfig, RenetClient},
+    transport::{client_connected, NetcodeClientPlugin},
+    RenetClientPlugin,
+};
+
+#[derive(Debug, Resource)]
+struct CurrentClientId(u64);
+
+#[derive(Resource)]
+struct BevyGameState(GameState);
+
+impl Default for BevyGameState {
+    fn default() -> Self {
+        Self {
+            0: GameState::default(),
+        }
+    }
+}
+
+#[derive(Resource, Deref, DerefMut)]
+struct GameUIState {
+    selected_tile: Option<usize>,
+}
+
+impl Default for GameUIState {
+    fn default() -> Self {
+        Self {
+            selected_tile: None,
+        }
+    }
+}
+
+#[derive(Event)]
+struct BevyGameEvent(GameEvent);
+
+// This id needs to be the same as the server is using
+const PROTOCOL_ID: u64 = 2878;
+
+fn main() {
+    // Get username from stdin args
+    let args = std::env::args().collect::<Vec<String>>();
+    let username = &args[1];
+
+    let (client, transport, client_id) = new_renet_client(&username).unwrap();
+    App::new()
+        // Lets add a nice dark grey background color
+        .insert_resource(ClearColor(Color::hex("282828").unwrap()))
+        .add_plugins(DefaultPlugins.set(WindowPlugin {
+            primary_window: Some(Window {
+                // Adding the username to the window title makes debugging a whole lot easier.
+                title: format!("TricTrac <{}>", username),
+                resolution: (1080.0, 1080.0).into(),
+                ..default()
+            }),
+            ..default()
+        }))
+        // Add our game state and register GameEvent as a bevy event
+        .insert_resource(BevyGameState::default())
+        .insert_resource(GameUIState::default())
+        .add_event::<BevyGameEvent>()
+        // Renet setup
+        .add_plugins(RenetClientPlugin)
+        .add_plugins(NetcodeClientPlugin)
+        .insert_resource(client)
+        .insert_resource(transport)
+        .insert_resource(CurrentClientId(client_id))
+        .add_systems(Startup, setup)
+        .add_systems(Update, (update_waiting_text, input, update_board, panic_on_error_system))
+        .add_systems(
+            PostUpdate,
+            receive_events_from_server.run_if(client_connected()),
+        )
+        .run();
+}
+
+////////// COMPONENTS //////////
+#[derive(Component)]
+struct UIRoot;
+
+#[derive(Component)]
+struct WaitingText;
+
+#[derive(Component)]
+struct Board {
+    squares: [Square; 26]
+}
+
+impl Default for Board {
+    fn default() -> Self {
+        Self {
+            squares: [Square { count: 0, color: None, position: 0}; 26]
+        }
+    }
+}
+
+impl Board {
+    fn square_at(&self, position: usize) -> Square {
+       self.squares[position] 
+    }
+}
+
+#[derive(Component, Clone, Copy)]
+struct Square {
+    count: usize,
+    color: Option<bool>,
+    position: usize,
+}
+
+////////// UPDATE SYSTEMS //////////
+fn update_board(
+    mut commands: Commands,
+    game_state: Res<BevyGameState>,
+    mut game_events: EventReader<BevyGameEvent>,
+    asset_server: Res<AssetServer>,
+) {
+    for event in game_events.iter() {
+        match event.0 {
+            GameEvent::Move { player_id, moves } => {
+                // trictrac positions, TODO : dépend de player_id
+                let (x, y) = if moves.0.get_to() < 13 { (13 - moves.0.get_to(), 1) } else { (moves.0.get_to() - 13, 0)};
+                let texture =
+                    asset_server.load(match game_state.0.players[&player_id].color {
+                        store::Color::Black => "tac.png",
+                        store::Color::White => "tic.png",
+                    });
+
+            info!("spawning tictac sprite");
+                commands.spawn(SpriteBundle {
+                    transform: Transform::from_xyz(
+                        83.0 * (x as f32 - 1.0),
+                        -30.0 + 540.0 * (y as f32 - 1.0),
+                        0.0,
+                    ),
+                    sprite: Sprite {
+                        custom_size: Some(Vec2::new(83.0, 83.0)),
+                        ..default()
+                    },
+                    texture: texture.into(),
+                    ..default()
+                });
+            }
+            _ => {}
+        }
+    }
+}
+
+fn update_waiting_text(mut text_query: Query<&mut Text, With<WaitingText>>, time: Res<Time>) {
+    if let Ok(mut text) = text_query.get_single_mut() {
+        let num_dots = (time.elapsed_seconds() as usize % 3) + 1;
+        text.sections[0].value = format!(
+            "Waiting for an opponent{}{}",
+            ".".repeat(num_dots as usize),
+            // Pad with spaces to avoid text changing width and dancing all around the screen 🕺
+            " ".repeat(3 - num_dots as usize)
+        );
+    }
+}
+
+fn input(
+    primary_query: Query<&Window, With<PrimaryWindow>>,
+    // windows: Res<Windows>,
+    input: Res<Input<MouseButton>>,
+    game_state: Res<BevyGameState>,
+    mut game_ui_state: ResMut<GameUIState>,
+    mut client: ResMut<RenetClient>,
+    client_id: Res<CurrentClientId>,
+) {
+    // We only want to handle inputs once we are ingame
+    if game_state.0.stage != store::Stage::InGame {
+        return;
+    }
+
+    let window = primary_query.get_single().unwrap();
+    if let Some(mouse_position) = window.cursor_position() {
+        // Determine the index of the tile that the mouse is currently over
+        // NOTE: This calculation assumes a fixed window size.
+        // That's fine for now, but consider using the windows size instead.
+        let mut tile_x: usize = (mouse_position.x / 83.0).floor() as usize;
+        let tile_y: usize = (mouse_position.y / 540.0).floor() as usize;
+        if tile_x > 5 {
+            // remove the middle bar offset
+            tile_x = tile_x - 1
+        } 
+        // let tile = tile_x + tile_y * 12;
+
+        // traduction en position backgammon
+        let tile = if tile_y == 0 {
+            13 + tile_x
+        } else {
+            12 - tile_x 
+        };
+
+        // If mouse is outside of board we do nothing
+        if 23 < tile {
+            return;
+        }
+
+        // If left mouse button is pressed, send a place tile event to the server
+        if input.just_pressed(MouseButton::Left) {
+            info!("select piece at tile {:?}", tile);
+            if game_ui_state.selected_tile.is_some() {
+                let from_tile = game_ui_state.selected_tile.unwrap();
+                info!("sending movement from: {:?} to: {:?} ", from_tile, tile);
+                let event = GameEvent::Move {
+                    player_id: client_id.0,
+                    moves: (
+                        CheckerMove::new(from_tile, tile).unwrap(),
+                        CheckerMove::new(from_tile, tile).unwrap()
+                        )
+                };
+                client.send_message(0, bincode::serialize(&event).unwrap());
+            }
+            game_ui_state.selected_tile = if game_ui_state.selected_tile.is_some() {
+                None
+            } else {
+                Some(tile)
+            }
+        }
+    }
+}
+
+////////// SETUP //////////
+fn setup(mut commands: Commands, asset_server: Res<AssetServer>) {
+    // Tric Trac is a 2D game
+    // To show 2D sprites we need a 2D camera
+    commands.spawn(Camera2dBundle::default());
+
+    // Spawn board background
+    commands.spawn(SpriteBundle {
+        transform: Transform::from_xyz(0.0, -30.0, 0.0),
+        sprite: Sprite {
+            custom_size: Some(Vec2::new(1080.0, 927.0)),
+            ..default()
+        },
+        texture: asset_server.load("board.png").into(),
+        ..default()
+    });
+
+    // Spawn pregame ui
+    commands
+        // A container that centers its children on the screen
+        .spawn(NodeBundle {
+            style: Style {
+                position_type: PositionType::Absolute,
+                left: Val::Px(0.0),
+                top: Val::Px(0.0),
+                width: Val::Percent(100.0),
+                height: Val::Percent(100.0),
+                align_items: AlignItems::Center,
+                justify_content: JustifyContent::Center,
+                ..default()
+            },
+            ..default()
+        })
+        .insert(UIRoot)
+        .with_children(|parent| {
+            // parent.spawn(Board::default()); // panic
+            parent
+                .spawn(TextBundle::from_section(
+                    "Waiting for an opponent...",
+                    TextStyle {
+                        font: asset_server.load("Inconsolata.ttf"),
+                        font_size: 24.0,
+                        color: Color::hex("ebdbb2").unwrap(),
+                    },
+                ))
+                .insert(WaitingText);
+        });
+}
+
+////////// RENET NETWORKING //////////
+// Creates a RenetClient thats already connected to a server.
+// Returns an Err if connection fails
+fn new_renet_client(
+    username: &String,
+) -> anyhow::Result<(RenetClient, NetcodeClientTransport, u64)> {
+    let client = RenetClient::new(ConnectionConfig::default());
+    let server_addr = "127.0.0.1:5000".parse()?;
+    let socket = UdpSocket::bind("127.0.0.1:0")?;
+    let current_time = SystemTime::now().duration_since(SystemTime::UNIX_EPOCH)?;
+    let client_id = current_time.as_millis() as u64;
+
+    // Place username in user data
+    let mut user_data = [0u8; NETCODE_USER_DATA_BYTES];
+    if username.len() > NETCODE_USER_DATA_BYTES - 8 {
+        panic!("Username is too big");
+    }
+    user_data[0..8].copy_from_slice(&(username.len() as u64).to_le_bytes());
+    user_data[8..username.len() + 8].copy_from_slice(username.as_bytes());
+
+    let authentication = ClientAuthentication::Unsecure {
+        server_addr,
+        client_id,
+        user_data: Some(user_data),
+        protocol_id: PROTOCOL_ID,
+    };
+    let transport = NetcodeClientTransport::new(current_time, authentication, socket).unwrap();
+
+    Ok((client, transport, client_id))
+}
+
+fn receive_events_from_server(
+    mut client: ResMut<RenetClient>,
+    mut game_state: ResMut<BevyGameState>,
+    mut game_events: EventWriter<BevyGameEvent>,
+) {
+    while let Some(message) = client.receive_message(0) {
+        // Whenever the server sends a message we know that it must be a game event
+        let event: GameEvent = bincode::deserialize(&message).unwrap();
+        trace!("{:#?}", event);
+
+        // We trust the server - It's always been good to us!
+        // No need to validate the events it is sending us
+        game_state.0.consume(&event);
+
+        // Send the event into the bevy event system so systems can react to it
+        game_events.send(BevyGameEvent(event));
+    }
+}
+
+// If any error is found we just panic
+fn panic_on_error_system(mut renet_error: EventReader<NetcodeTransportError>) {
+    for e in renet_error.iter() {
+        panic!("{}", e);
+    }
+}

client_tui/Cargo.toml

@@ -0,0 +1,14 @@
+[package]
+name = "client_tui"
+version = "0.1.0"
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+anyhow = "1.0.89"
+bincode = "1.3.3"
+crossterm = "0.28.1"
+ratatui = "0.28.1"
+# renet = "0.0.13"
+store = { path = "../store" }

client_tui/src/app.rs

@@ -0,0 +1,53 @@
+// Application.
+#[derive(Debug, Default)]
+pub struct App {
+    // should the application exit?
+    pub should_quit: bool,
+    // counter
+    pub counter: u8,
+}
+
+impl App {
+    // Constructs a new instance of [`App`].
+    pub fn new() -> Self {
+        Self::default()
+    }
+
+    // Handles the tick event of the terminal.
+    pub fn tick(&self) {}
+
+    // Set running to false to quit the application.
+    pub fn quit(&mut self) {
+        self.should_quit = true;
+    }
+
+    pub fn increment_counter(&mut self) {
+        if let Some(res) = self.counter.checked_add(1) {
+            self.counter = res;
+        }
+    }
+
+    pub fn decrement_counter(&mut self) {
+        if let Some(res) = self.counter.checked_sub(1) {
+            self.counter = res;
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    #[test]
+    fn test_app_increment_counter() {
+        let mut app = App::default();
+        app.increment_counter();
+        assert_eq!(app.counter, 1);
+    }
+
+    #[test]
+    fn test_app_decrement_counter() {
+        let mut app = App::default();
+        app.decrement_counter();
+        assert_eq!(app.counter, 0);
+    }
+}

client_tui/src/event.rs

@@ -0,0 +1,87 @@
+use std::{
+    sync::mpsc,
+    thread,
+    time::{Duration, Instant},
+};
+
+use anyhow::Result;
+use crossterm::event::{self, Event as CrosstermEvent, KeyEvent, MouseEvent};
+
+// Terminal events.
+#[derive(Clone, Copy, Debug)]
+pub enum Event {
+    // Terminal tick.
+    Tick,
+    // Key press.
+    Key(KeyEvent),
+    // Mouse click/scroll.
+    Mouse(MouseEvent),
+    // Terminal resize.
+    Resize(u16, u16),
+}
+
+// Terminal event handler.
+#[derive(Debug)]
+pub struct EventHandler {
+    // Event sender channel.
+    #[allow(dead_code)]
+    sender: mpsc::Sender<Event>,
+    // Event receiver channel.
+    receiver: mpsc::Receiver<Event>,
+    // Event handler thread.
+    #[allow(dead_code)]
+    handler: thread::JoinHandle<()>,
+}
+
+impl EventHandler {
+    // Constructs a new instance of [`EventHandler`].
+    pub fn new(tick_rate: u64) -> Self {
+        let tick_rate = Duration::from_millis(tick_rate);
+        let (sender, receiver) = mpsc::channel();
+        let handler = {
+            let sender = sender.clone();
+            thread::spawn(move || {
+                let mut last_tick = Instant::now();
+                loop {
+                    let timeout = tick_rate
+                        .checked_sub(last_tick.elapsed())
+                        .unwrap_or(tick_rate);
+
+                    if event::poll(timeout).expect("no events available") {
+                        match event::read().expect("unable to read event") {
+                            CrosstermEvent::Key(e) => {
+                                if e.kind == event::KeyEventKind::Press {
+                                    sender.send(Event::Key(e))
+                                } else {
+                                    Ok(()) // ignore KeyEventKind::Release on windows
+                                }
+                            }
+                            CrosstermEvent::Mouse(e) => sender.send(Event::Mouse(e)),
+                            CrosstermEvent::Resize(w, h) => sender.send(Event::Resize(w, h)),
+                            _ => unimplemented!(),
+                        }
+                        .expect("failed to send terminal event")
+                    }
+
+                    if last_tick.elapsed() >= tick_rate {
+                        sender.send(Event::Tick).expect("failed to send tick event");
+                        last_tick = Instant::now();
+                    }
+                }
+            })
+        };
+        Self {
+            sender,
+            receiver,
+            handler,
+        }
+    }
+
+    // Receive the next event from the handler thread.
+    //
+    // This function will always block the current thread if
+    // there is no data available and it's possible for more data to be sent.
+    pub fn next(&self) -> Result<Event> {
+        Ok(self.receiver.recv()?)
+    }
+}

client_tui/src/main.rs

@@ -0,0 +1,50 @@
+// Application.
+pub mod app;
+
+// Terminal events handler.
+pub mod event;
+
+// Widget renderer.
+pub mod ui;
+
+// Terminal user interface.
+pub mod tui;
+
+// Application updater.
+pub mod update;
+
+use anyhow::Result;
+use app::App;
+use event::{Event, EventHandler};
+use ratatui::{backend::CrosstermBackend, Terminal};
+use tui::Tui;
+use update::update;
+
+fn main() -> Result<()> {
+    // Create an application.
+    let mut app = App::new();
+
+    // Initialize the terminal user interface.
+    let backend = CrosstermBackend::new(std::io::stderr());
+    let terminal = Terminal::new(backend)?;
+    let events = EventHandler::new(250);
+    let mut tui = Tui::new(terminal, events);
+    tui.enter()?;
+
+    // Start the main loop.
+    while !app.should_quit {
+        // Render the user interface.
+        tui.draw(&mut app)?;
+        // Handle events.
+        match tui.events.next()? {
+            Event::Tick => {}
+            Event::Key(key_event) => update(&mut app, key_event),
+            Event::Mouse(_) => {}
+            Event::Resize(_, _) => {}
+        };
+    }
+
+    // Exit the user interface.
+    tui.exit()?;
+    Ok(())
+}

client_tui/src/tui.rs

@@ -0,0 +1,77 @@
+use std::{io, panic};
+
+use anyhow::Result;
+use crossterm::{
+    event::{DisableMouseCapture, EnableMouseCapture},
+    terminal::{self, EnterAlternateScreen, LeaveAlternateScreen},
+};
+
+pub type CrosstermTerminal = ratatui::Terminal<ratatui::backend::CrosstermBackend<std::io::Stderr>>;
+
+use crate::{app::App, event::EventHandler, ui};
+
+// Representation of a terminal user interface.
+//
+// It is responsible for setting up the terminal,
+// initializing the interface and handling the draw events.
+pub struct Tui {
+    // Interface to the Terminal.
+    terminal: CrosstermTerminal,
+    // Terminal event handler.
+    pub events: EventHandler,
+}
+
+impl Tui {
+    // Constructs a new instance of [`Tui`].
+    pub fn new(terminal: CrosstermTerminal, events: EventHandler) -> Self {
+        Self { terminal, events }
+    }
+
+    // Initializes the terminal interface.
+    //
+    // It enables the raw mode and sets terminal properties.
+    pub fn enter(&mut self) -> Result<()> {
+        terminal::enable_raw_mode()?;
+        crossterm::execute!(io::stderr(), EnterAlternateScreen, EnableMouseCapture)?;
+
+        // Define a custom panic hook to reset the terminal properties.
+        // This way, you won't have your terminal messed up if an unexpected error happens.
+        let panic_hook = panic::take_hook();
+        panic::set_hook(Box::new(move |panic| {
+            Self::reset().expect("failed to reset the terminal");
+            panic_hook(panic);
+        }));
+
+        self.terminal.hide_cursor()?;
+        self.terminal.clear()?;
+        Ok(())
+    }
+
+    // [`Draw`] the terminal interface by [`rendering`] the widgets.
+    //
+    // [`Draw`]: tui::Terminal::draw
+    // [`rendering`]: crate::ui:render
+    pub fn draw(&mut self, app: &mut App) -> Result<()> {
+        self.terminal.draw(|frame| ui::render(app, frame))?;
+        Ok(())
+    }
+
+    // Resets the terminal interface.
+    //
+    // This function is also used for the panic hook to revert
+    // the terminal properties if unexpected errors occur.
+    fn reset() -> Result<()> {
+        terminal::disable_raw_mode()?;
+        crossterm::execute!(io::stderr(), LeaveAlternateScreen, DisableMouseCapture)?;
+        Ok(())
+    }
+
+    // Exits the terminal interface.
+    //
+    // It disables the raw mode and reverts back the terminal properties.
+    pub fn exit(&mut self) -> Result<()> {
+        Self::reset()?;
+        self.terminal.show_cursor()?;
+        Ok(())
+    }
+}

client_tui/src/ui.rs

@@ -0,0 +1,30 @@
+use ratatui::{
+    prelude::{Alignment, Frame},
+    style::{Color, Style},
+    widgets::{Block, BorderType, Borders, Paragraph},
+};
+
+use crate::app::App;
+
+pub fn render(app: &mut App, f: &mut Frame) {
+    f.render_widget(
+        Paragraph::new(format!(
+            "
+        Press `Esc`, `Ctrl-C` or `q` to stop running.\n\
+        Press `j` and `k` to increment and decrement the counter respectively.\n\
+        Counter: {}
+      ",
+            app.counter
+        ))
+        .block(
+            Block::default()
+                .title("Counter App")
+                .title_alignment(Alignment::Center)
+                .borders(Borders::ALL)
+                .border_type(BorderType::Rounded),
+        )
+        .style(Style::default().fg(Color::Yellow))
+        .alignment(Alignment::Center),
+        f.area(),
+    )
+}

client_tui/src/update.rs

@@ -0,0 +1,17 @@
+use crossterm::event::{KeyCode, KeyEvent, KeyModifiers};
+
+use crate::app::App;
+
+pub fn update(app: &mut App, key_event: KeyEvent) {
+    match key_event.code {
+        KeyCode::Esc | KeyCode::Char('q') => app.quit(),
+        KeyCode::Char('c') | KeyCode::Char('C') => {
+            if key_event.modifiers == KeyModifiers::CONTROL {
+                app.quit()
+            }
+        }
+        KeyCode::Right | KeyCode::Char('j') => app.increment_counter(),
+        KeyCode::Left | KeyCode::Char('k') => app.decrement_counter(),
+        _ => {}
+    };
+}

justfile

@@ -22,6 +22,9 @@ profile:
 pythonlib:
   maturin build -m store/Cargo.toml --release
   pip install --no-deps --force-reinstall --prefix .devenv/state/venv target/wheels/*.whl
+trainsimple:
+  cargo build --release --bin=train_dqn_simple
+  LD_LIBRARY_PATH=./target/release ./target/release/train_dqn_simple | tee /tmp/train.out
 trainbot algo:
   #python ./store/python/trainModel.py
   # cargo run --bin=train_dqn # ok

server/Cargo.toml

@@ -0,0 +1,14 @@
+[package]
+name = "trictrac-server"
+version = "0.1.0"
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+store = { path = "../store" }
+env_logger = "0.10.0"
+log = "0.4.20"
+pico-args = "0.5.0"
+renet = "0.0.13"
+bincode = "1.3.3"

server/src/main.rs

@@ -0,0 +1,147 @@
+use log::{info, trace, warn};
+use std::net::{IpAddr, Ipv4Addr, SocketAddr, UdpSocket};
+use std::thread;
+use std::time::{Duration, Instant, SystemTime};
+
+use renet::{
+    transport::{
+        NetcodeServerTransport, ServerAuthentication, ServerConfig, NETCODE_USER_DATA_BYTES,
+    },
+    ConnectionConfig, RenetServer, ServerEvent,
+};
+
+// Only clients that can provide the same PROTOCOL_ID that the server is using will be able to connect.
+// This can be used to make sure players use the most recent version of the client for instance.
+pub const PROTOCOL_ID: u64 = 2878;
+
+/// Utility function for extracting a players name from renet user data
+fn name_from_user_data(user_data: &[u8; NETCODE_USER_DATA_BYTES]) -> String {
+    let mut buffer = [0u8; 8];
+    buffer.copy_from_slice(&user_data[0..8]);
+    let mut len = u64::from_le_bytes(buffer) as usize;
+    len = len.min(NETCODE_USER_DATA_BYTES - 8);
+    let data = user_data[8..len + 8].to_vec();
+    String::from_utf8(data).unwrap()
+}
+
+fn main() {
+    env_logger::init();
+
+    let mut server = RenetServer::new(ConnectionConfig::default());
+
+    // Setup transport layer
+    const SERVER_ADDR: SocketAddr = SocketAddr::new(IpAddr::V4(Ipv4Addr::new(127, 0, 0, 1)), 5000);
+    let socket: UdpSocket = UdpSocket::bind(SERVER_ADDR).unwrap();
+    let server_config = ServerConfig {
+        max_clients: 2,
+        protocol_id: PROTOCOL_ID,
+        public_addr: SERVER_ADDR,
+        authentication: ServerAuthentication::Unsecure,
+    };
+    let current_time = SystemTime::now()
+        .duration_since(SystemTime::UNIX_EPOCH)
+        .unwrap();
+    let mut transport = NetcodeServerTransport::new(current_time, server_config, socket).unwrap();
+
+    trace!("❂ TricTrac server listening on {SERVER_ADDR}");
+
+    let mut game_state = store::GameState::default();
+    let mut last_updated = Instant::now();
+    loop {
+        // Update server time
+        let now = Instant::now();
+        let delta_time = now - last_updated;
+        server.update(delta_time);
+        transport.update(delta_time, &mut server).unwrap();
+        last_updated = now;
+
+        // Receive connection events from clients
+        while let Some(event) = server.get_event() {
+            match event {
+                ServerEvent::ClientConnected { client_id } => {
+                    let user_data = transport.user_data(client_id).unwrap();
+
+                    // Tell the recently joined player about the other player
+                    for (player_id, player) in game_state.players.iter() {
+                        let event = store::GameEvent::PlayerJoined {
+                            player_id: *player_id,
+                            name: player.name.clone(),
+                        };
+                        server.send_message(client_id, 0, bincode::serialize(&event).unwrap());
+                    }
+
+                    // Add the new player to the game
+                    let event = store::GameEvent::PlayerJoined {
+                        player_id: client_id,
+                        name: name_from_user_data(&user_data),
+                    };
+                    game_state.consume(&event);
+
+                    // Tell all players that a new player has joined
+                    server.broadcast_message(0, bincode::serialize(&event).unwrap());
+
+                    info!("🎉 Client {client_id} connected.");
+                    // In TicTacTussle the game can begin once two players has joined
+                    if game_state.players.len() == 2 {
+                        let event = store::GameEvent::BeginGame {
+                            goes_first: client_id,
+                        };
+                        game_state.consume(&event);
+                        server.broadcast_message(0, bincode::serialize(&event).unwrap());
+                        trace!("The game gas begun");
+                    }
+                }
+                ServerEvent::ClientDisconnected {
+                    client_id,
+                    reason: _,
+                } => {
+                    // First consume a disconnect event
+                    let event = store::GameEvent::PlayerDisconnected {
+                        player_id: client_id,
+                    };
+                    game_state.consume(&event);
+                    server.broadcast_message(0, bincode::serialize(&event).unwrap());
+                    info!("Client {client_id} disconnected");
+
+                    // Then end the game, since tic tac toe can't go on with a single player
+                    let event = store::GameEvent::EndGame {
+                        reason: store::EndGameReason::PlayerLeft {
+                            player_id: client_id,
+                        },
+                    };
+                    game_state.consume(&event);
+                    server.broadcast_message(0, bincode::serialize(&event).unwrap());
+
+                    // NOTE: Since we don't authenticate users we can't do any reconnection attempts.
+                    // We simply have no way to know if the next user is the same as the one that disconnected.
+                }
+            }
+        }
+
+        // Receive GameEvents from clients. Broadcast valid events.
+        for client_id in server.clients_id().into_iter() {
+            while let Some(message) = server.receive_message(client_id, 0) {
+                if let Ok(event) = bincode::deserialize::<store::GameEvent>(&message) {
+                    if game_state.validate(&event) {
+                        game_state.consume(&event);
+                        trace!("Player {client_id} sent:\n\t{event:#?}");
+                        server.broadcast_message(0, bincode::serialize(&event).unwrap());
+
+                        // Determine if a player has won the game
+                        if let Some(winner) = game_state.determine_winner() {
+                            let event = store::GameEvent::EndGame {
+                                reason: store::EndGameReason::PlayerWon { winner },
+                            };
+                            server.broadcast_message(0, bincode::serialize(&event).unwrap());
+                        }
+                    } else {
+                        warn!("Player {client_id} sent invalid event:\n\t{event:#?}");
+                    }
+                }
+            }
+        }
+
+        transport.send_packets(&mut server);
+        thread::sleep(Duration::from_millis(50));
+    }
+}

store/src/game.rs

@@ -742,10 +742,6 @@ impl GameState {
         });
     }
 
-    pub fn mark_points_for_bot_training(&mut self, player_id: PlayerId, points: u8) -> bool {
-        self.mark_points(player_id, points)
-    }
-
     fn mark_points(&mut self, player_id: PlayerId, points: u8) -> bool {
         // Update player points and holes
         let mut new_hole = false;