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Merge branch 'release/v0.1.1'

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Henri Bourcereau 2026-01-15 17:14:40 +01:00
parent 2ef1f7ee50 de303ad574
commit 228dc5d50c
57 changed files with 1901 additions and 4086 deletions
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2424
Cargo.lock generated
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2
Cargo.toml
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@ -1,4 +1,4 @@
[workspace] [workspace]
resolver = "2" resolver = "2"
members = ["client_tui", "client_cli", "bot", "server", "store"] members = ["client_cli", "bot", "store"]

38
README.md
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@ -1,7 +1,41 @@
# Trictrac # Trictrac
Game of [Trictrac](https://en.wikipedia.org/wiki/Trictrac) in rust. This is a game of [Trictrac](https://en.wikipedia.org/wiki/Trictrac) rust implementation.
wip The project is on its early stages.
Rules (without "schools") are implemented, as well as a rudimentary terminal interface which allow you to play against a bot which plays randomly.
Training of AI bots is the work in progress.
## Usage
`cargo run --bin=client_cli -- --bot random`
## Roadmap
- [x] rules
- [x] command line interface
- [x] basic bot (random play)
- [ ] AI bot
- [ ] network game
- [ ] web client
## Code structure
- game rules and game state are implemented in the _store/_ folder.
- the command-line application is implemented in _client_cli/_; it allows you to play against a bot, or to have two bots play against each other
- the bots algorithms and the training of their models are implemented in the _bot/_ folder
### _store_ package
The game state is defined by the `GameState` struct in _store/src/game.rs_. The `to_string_id()` method allows this state to be encoded compactly in a string (without the played moves history). For a more readable textual representation, the `fmt::Display` trait is implemented.
### _client_cli_ package
`client_cli/src/game_runner.rs` contains the logic to make two bots play against each other.
### _bot_ package
- `bot/src/strategy/default.rs` contains the code for a basic bot strategy: it determines the list of valid moves (using the `get_possible_moves_sequences` method of `store::MoveRules`) and simply executes the first move in the list.
- `bot/src/strategy/dqnburn.rs` is another bot strategy that uses a reinforcement learning trained model with the DQN algorithm via the burn library (<https://burn.dev/>).
- `bot/scripts/trains.sh` allows you to train agents using different algorithms (DQN, PPO, SAC).

6
bot/Cargo.toml
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@ -9,10 +9,6 @@ edition = "2021"
name = "burn_train" name = "burn_train"
path = "src/burnrl/main.rs" path = "src/burnrl/main.rs"
[[bin]]
name = "train_dqn_simple"
path = "src/dqn_simple/main.rs"
[dependencies] [dependencies]
pretty_assertions = "1.4.0" pretty_assertions = "1.4.0"
serde = { version = "1.0", features = ["derive"] } serde = { version = "1.0", features = ["derive"] }
@ -20,7 +16,7 @@ serde_json = "1.0"
store = { path = "../store" } store = { path = "../store" }
rand = "0.8" rand = "0.8"
env_logger = "0.10" env_logger = "0.10"
burn = { version = "0.17", features = ["ndarray", "autodiff"] } burn = { version = "0.18", features = ["ndarray", "autodiff"] }
burn-rl = { git = "https://github.com/yunjhongwu/burn-rl-examples.git", package = "burn-rl" } burn-rl = { git = "https://github.com/yunjhongwu/burn-rl-examples.git", package = "burn-rl" }
log = "0.4.20" log = "0.4.20"
confy = "1.0.0" confy = "1.0.0"

194
bot/src/burnrl/algos/dqn_big.rs
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@ -1,194 +0,0 @@
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()
}

3
bot/src/burnrl/algos/mod.rs
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@ -1,9 +1,6 @@
pub mod dqn; pub mod dqn;
pub mod dqn_big;
pub mod dqn_valid; pub mod dqn_valid;
pub mod ppo; pub mod ppo;
pub mod ppo_big;
pub mod ppo_valid; pub mod ppo_valid;
pub mod sac; pub mod sac;
pub mod sac_big;
pub mod sac_valid; pub mod sac_valid;

191
bot/src/burnrl/algos/ppo_big.rs
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@ -1,191 +0,0 @@
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()
}

222
bot/src/burnrl/algos/sac_big.rs
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@ -1,222 +0,0 @@
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()
}

12
bot/src/burnrl/environment.rs
View file

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

469
bot/src/burnrl/environment_big.rs
View file

@ -1,469 +0,0 @@
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
}
}

85
bot/src/burnrl/environment_valid.rs
View file

@ -1,9 +1,12 @@
use crate::training_common_big; use crate::training_common;
use burn::{prelude::Backend, tensor::Tensor}; use burn::{prelude::Backend, tensor::Tensor};
use burn_rl::base::{Action, Environment, Snapshot, State}; use burn_rl::base::{Action, Environment, Snapshot, State};
use rand::{thread_rng, Rng}; use rand::{thread_rng, Rng};
use store::{GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage}; 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 /// État du jeu Trictrac pour burn-rl
#[derive(Debug, Clone, Copy)] #[derive(Debug, Clone, Copy)]
pub struct TrictracState { pub struct TrictracState {
@ -214,16 +217,16 @@ impl TrictracEnvironment {
const REWARD_RATIO: f32 = 1.0; const REWARD_RATIO: f32 = 1.0;
/// Convertit une action burn-rl vers une action Trictrac /// Convertit une action burn-rl vers une action Trictrac
pub fn convert_action(action: TrictracAction) -> Option<training_common_big::TrictracAction> { pub fn convert_action(action: TrictracAction) -> Option<training_common::TrictracAction> {
training_common_big::TrictracAction::from_action_index(action.index.try_into().unwrap()) training_common::TrictracAction::from_action_index(action.index.try_into().unwrap())
} }
/// Convertit l'index d'une action au sein des actions valides vers une action Trictrac /// Convertit l'index d'une action au sein des actions valides vers une action Trictrac
fn convert_valid_action_index( fn convert_valid_action_index(
&self, &self,
action: TrictracAction, action: TrictracAction,
) -> Option<training_common_big::TrictracAction> { ) -> Option<training_common::TrictracAction> {
use training_common_big::get_valid_actions; use training_common::get_valid_actions;
// Obtenir les actions valides dans le contexte actuel // Obtenir les actions valides dans le contexte actuel
let valid_actions = get_valid_actions(&self.game); let valid_actions = get_valid_actions(&self.game);
@ -240,72 +243,19 @@ impl TrictracEnvironment {
/// Exécute une action Trictrac dans le jeu /// Exécute une action Trictrac dans le jeu
// fn execute_action( // fn execute_action(
// &mut self, // &mut self,
// action: training_common_big::TrictracAction, // action: training_common::TrictracAction,
// ) -> Result<f32, Box<dyn std::error::Error>> { // ) -> Result<f32, Box<dyn std::error::Error>> {
fn execute_action(&mut self, action: training_common_big::TrictracAction) -> (f32, bool) { fn execute_action(&mut self, action: training_common::TrictracAction) -> (f32, bool) {
use training_common_big::TrictracAction; use training_common::TrictracAction;
let mut reward = 0.0; let mut reward = 0.0;
let mut is_rollpoint = false; 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 // Appliquer l'événement si valide
if let Some(event) = event { if let Some(event) = action.to_event(&self.game) {
if self.game.validate(&event) { if self.game.validate(&event) {
self.game.consume(&event); self.game.consume(&event);
// reward += REWARD_VALID_MOVE;
// Simuler le résultat des dés après un Roll // Simuler le résultat des dés après un Roll
if matches!(action, TrictracAction::Roll) { if matches!(action, TrictracAction::Roll) {
let mut rng = thread_rng(); let mut rng = thread_rng();
@ -319,7 +269,7 @@ impl TrictracEnvironment {
if self.game.validate(&dice_event) { if self.game.validate(&dice_event) {
self.game.consume(&dice_event); self.game.consume(&dice_event);
let (points, adv_points) = self.game.dice_points; let (points, adv_points) = self.game.dice_points;
reward += Self::REWARD_RATIO * (points - adv_points) as f32; reward += REWARD_RATIO * (points as f32 - adv_points as f32);
if points > 0 { if points > 0 {
is_rollpoint = true; is_rollpoint = true;
// println!("info: rolled for {reward}"); // println!("info: rolled for {reward}");
@ -331,9 +281,12 @@ impl TrictracEnvironment {
// Pénalité pour action invalide // Pénalité pour action invalide
// on annule les précédents reward // on annule les précédents reward
// et on indique une valeur reconnaissable pour statistiques // et on indique une valeur reconnaissable pour statistiques
println!("info: action invalide -> err_reward"); reward = ERROR_REWARD;
reward = Self::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) (reward, is_rollpoint)

35
bot/src/burnrl/main.rs
View file

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

1
bot/src/burnrl/mod.rs
View file

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

153
bot/src/dqn_simple/dqn_model.rs
View file

@ -1,153 +0,0 @@
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)
}
}

494
bot/src/dqn_simple/dqn_trainer.rs
View file

@ -1,494 +0,0 @@
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(())
}
}

109
bot/src/dqn_simple/main.rs
View file

@ -1,109 +0,0 @@
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");
}

2
bot/src/dqn_simple/mod.rs
View file

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

3
bot/src/lib.rs
View file

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

174
bot/src/strategy/dqn.rs
View file

@ -1,174 +0,0 @@
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())
}
}
}

1
bot/src/strategy/mod.rs
View file

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

17
bot/src/training_common.rs
View file

@ -1,9 +1,15 @@
/// 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::cmp::{max, min};
use std::fmt::{Debug, Display, Formatter}; use std::fmt::{Debug, Display, Formatter};
use serde::{Deserialize, Serialize}; use serde::{Deserialize, Serialize};
use store::{CheckerMove, GameEvent, GameState}; use store::{CheckerMove, GameEvent, GameState};
// 1 (Roll) + 1 (Go) + mouvements possibles
// Pour les mouvements : 2*16*16 = 514 (choix du dé + choix de la dame 0-15 pour chaque from)
pub const ACTION_SPACE_SIZE: usize = 514;
/// Types d'actions possibles dans le jeu /// Types d'actions possibles dans le jeu
#[derive(Debug, Copy, Clone, Eq, Serialize, Deserialize, PartialEq)] #[derive(Debug, Copy, Clone, Eq, Serialize, Deserialize, PartialEq)]
pub enum TrictracAction { pub enum TrictracAction {
@ -158,10 +164,7 @@ impl TrictracAction {
/// Retourne la taille de l'espace d'actions total /// Retourne la taille de l'espace d'actions total
pub fn action_space_size() -> usize { pub fn action_space_size() -> usize {
// 1 (Roll) + 1 (Go) + mouvements possibles ACTION_SPACE_SIZE
// 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 * 16 * 16) // = 514
} }
// pub fn to_game_event(&self, player_id: PlayerId, dice: Dice) -> GameEvent { // pub fn to_game_event(&self, player_id: PlayerId, dice: Dice) -> GameEvent {
@ -225,7 +228,11 @@ pub fn get_valid_actions(game_state: &crate::GameState) -> Vec<TrictracAction> {
} }
TurnStage::Move => { TurnStage::Move => {
let rules = store::MoveRules::new(&color, &game_state.board, game_state.dice); let rules = store::MoveRules::new(&color, &game_state.board, game_state.dice);
let possible_moves = rules.get_possible_moves_sequences(true, vec![]); 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 // Modififier checker_moves_to_trictrac_action si on doit gérer Black
assert_eq!(color, store::Color::White); assert_eq!(color, store::Color::White);

262
bot/src/training_common_big.rs
View file

@ -1,262 +0,0 @@
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 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,
));
}
}
}
}
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));
}
}

17
bot/src/trictrac_board.rs
View file

@ -6,10 +6,11 @@ use board_game::board::{
use board_game::impl_unit_symmetry_board; use board_game::impl_unit_symmetry_board;
use internal_iterator::InternalIterator; use internal_iterator::InternalIterator;
use std::fmt; use std::fmt;
use std::hash::Hash;
use std::ops::ControlFlow; use std::ops::ControlFlow;
use store::Color; use store::Color;
#[derive(Clone, Debug, Eq, PartialEq)] #[derive(Clone, Debug, Eq, PartialEq, Hash)]
pub struct TrictracBoard(crate::GameState); pub struct TrictracBoard(crate::GameState);
impl Default for TrictracBoard { impl Default for TrictracBoard {
@ -77,6 +78,20 @@ impl BoardGameBoard for TrictracBoard {
} }
} }
impl TrictracBoard {
pub fn inner(&self) -> &crate::GameState {
&self.0
}
pub fn to_fen(&self) -> String {
self.0.to_string_id()
}
pub fn from_fen(fen: &str) -> Result<TrictracBoard, String> {
crate::GameState::from_string_id(fen).map(TrictracBoard)
}
}
impl<'a> BoardMoves<'a, TrictracBoard> for TrictracBoard { impl<'a> BoardMoves<'a, TrictracBoard> for TrictracBoard {
type AllMovesIterator = TrictracAllMovesIterator; type AllMovesIterator = TrictracAllMovesIterator;
type AvailableMovesIterator = TrictracAvailableMovesIterator<'a>; type AvailableMovesIterator = TrictracAvailableMovesIterator<'a>;

8
client_bevy/.cargo/config.toml
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@ -1,8 +0,0 @@
[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

14
client_bevy/Cargo.toml
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@ -1,14 +0,0 @@
[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" }

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334
client_bevy/src/main.rs
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@ -1,334 +0,0 @@
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);
}
}

12
client_cli/src/app.rs
View file

@ -1,5 +1,5 @@
use bot::{ use bot::{
BotStrategy, DefaultStrategy, DqnBurnStrategy, DqnStrategy, ErroneousStrategy, RandomStrategy, BotStrategy, DefaultStrategy, DqnBurnStrategy, ErroneousStrategy, RandomStrategy,
StableBaselines3Strategy, StableBaselines3Strategy,
}; };
use itertools::Itertools; use itertools::Itertools;
@ -25,8 +25,8 @@ pub struct App {
impl App { impl App {
// Constructs a new instance of [`App`]. // Constructs a new instance of [`App`].
pub fn new(args: AppArgs) -> Self { pub fn new(args: AppArgs) -> Self {
let bot_strategies: Vec<Box<dyn BotStrategy>> = args let bot_strategies: Vec<Box<dyn BotStrategy>> =
.bot args.bot
.as_deref() .as_deref()
.map(|str_bots| { .map(|str_bots| {
str_bots str_bots
@ -43,7 +43,6 @@ impl App {
} }
"ai" => Some(Box::new(StableBaselines3Strategy::default()) "ai" => Some(Box::new(StableBaselines3Strategy::default())
as Box<dyn BotStrategy>), as Box<dyn BotStrategy>),
"dqn" => Some(Box::new(DqnStrategy::default()) as Box<dyn BotStrategy>),
"dqnburn" => { "dqnburn" => {
Some(Box::new(DqnBurnStrategy::default()) as Box<dyn BotStrategy>) Some(Box::new(DqnBurnStrategy::default()) as Box<dyn BotStrategy>)
} }
@ -52,11 +51,6 @@ impl App {
Some(Box::new(StableBaselines3Strategy::new(path)) Some(Box::new(StableBaselines3Strategy::new(path))
as Box<dyn BotStrategy>) as Box<dyn BotStrategy>)
} }
s if s.starts_with("dqn:") => {
let path = s.trim_start_matches("dqn:");
Some(Box::new(DqnStrategy::new_with_model(path))
as Box<dyn BotStrategy>)
}
s if s.starts_with("dqnburn:") => { s if s.starts_with("dqnburn:") => {
let path = s.trim_start_matches("dqnburn:"); let path = s.trim_start_matches("dqnburn:");
Some(Box::new(DqnBurnStrategy::new_with_model(&path.to_string())) Some(Box::new(DqnBurnStrategy::new_with_model(&path.to_string()))

14
client_tui/Cargo.toml
View file

@ -1,14 +0,0 @@
[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" }

53
client_tui/src/app.rs
View file

@ -1,53 +0,0 @@
// 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);
}
}

87
client_tui/src/event.rs
View file

@ -1,87 +0,0 @@
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()?)
}
}

50
client_tui/src/main.rs
View file

@ -1,50 +0,0 @@
// 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(())
}

77
client_tui/src/tui.rs
View file

@ -1,77 +0,0 @@
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(())
}
}

30
client_tui/src/ui.rs
View file

@ -1,30 +0,0 @@
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(),
)
}

17
client_tui/src/update.rs
View file

@ -1,17 +0,0 @@
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(),
_ => {}
};
}

0
doc/refs/claudeAIquestion.md → doc/ai/history/claudeAIquestion.md
View file

0
doc/refs/claudeAIquestionOnlyRust.md → doc/ai/history/claudeAIquestionOnlyRust.md
View file

0
doc/traité.md → doc/book/traité.md
View file

19
doc/refs/geminiQuestions.md
View file

@ -1,19 +0,0 @@
# Description du projet
Je développe un jeu de TricTrac (<https://fr.wikipedia.org/wiki/Trictrac>) dans le langage rust.
Pour le moment je me concentre sur l'application en ligne de commande simple, donc ne t'occupe pas des dossiers 'client_bevy', 'client_tui', et 'server' qui ne seront utilisés que pour de prochaines évolutions.
Les règles du jeu et l'état d'une partie sont implémentées dans 'store', l'application ligne de commande est implémentée dans 'client_cli', elle permet déjà de jouer contre un bot, ou de faire jouer deux bots l'un contre l'autre.
Les stratégies de bots sont implémentées dans le dossier 'bot'.
Plus précisément, l'état du jeu est défini par le struct GameState dans store/src/game.rs, la méthode to_string_id() permet de coder cet état de manière compacte dans une chaîne de caractères, mais il n'y a pas l'historique des coups joués. Il y a aussi fmt::Display d'implémenté pour une representation textuelle plus lisible.
'client_cli/src/game_runner.rs' contient la logique permettant de faire jouer deux bots l'un contre l'autre.
'bot/src/strategy/default.rs' contient le code d'une stratégie de bot basique : il détermine la liste des mouvements valides (avec la méthode get_possible_moves_sequences de store::MoveRules) et joue simplement le premier de la liste.
Je cherche maintenant à ajouter des stratégies de bot plus fortes en entrainant un agent/bot par reinforcement learning.
J'utilise la bibliothèque burn (<https://burn.dev/>).
Une version utilisant l'algorithme DQN peut être lancée avec `cargo run --bin=burn_train -- dqn`). Elle effectue un entraînement, sauvegarde les données du modèle obtenu puis recharge le modèle depuis le disque pour tester l'agent. L'entraînement est fait dans la fonction 'run' du fichier bot/src/burnrl/dqn_model.rs, la sauvegarde du modèle dans la fonction 'save_model' et le chargement dans la fonction 'load_model'.
J'essaie de faire l'équivalent avec les algorithmes PPO (fichier bot/src/burnrl/ppo_model.rs) et SAC (fichier bot/src/burnrl/sac_model.rs) : les fonctions 'run' sont implémentées mais pas les fonctions 'save_model' et 'load_model'. Peux-tu les implémenter ?

2
doc/refs/inspirations.md
View file

@ -22,6 +22,7 @@ nombres aléatoires avec seed : <https://richard.dallaway.com/posts/2021-01-04-r
- rayon ( sync <-> parallel ) - rayon ( sync <-> parallel )
- front : yew + tauri - front : yew + tauri
- egui - egui
- <https://docs.rs/board-game/latest/board_game/> - <https://docs.rs/board-game/latest/board_game/>
@ -31,6 +32,7 @@ nombres aléatoires avec seed : <https://richard.dallaway.com/posts/2021-01-04-r
- <https://www.mattkeeter.com/projects/pont/> - <https://www.mattkeeter.com/projects/pont/>
- <https://github.com/jackadamson/onitama> (wasm, rooms) - <https://github.com/jackadamson/onitama> (wasm, rooms)
- <https://github.com/UkoeHB/renet2> - <https://github.com/UkoeHB/renet2>
- <https://github.com/UkoeHB/bevy_simplenet>
## Others ## Others

0
doc/diagrammes.md → doc/specs/diagrammes.md
View file

0
doc/refs/specifications.md → doc/specs/stateEncoding.md
View file

0
doc/store.puml → doc/specs/store.puml
View file

0
doc/vocabulary.md → doc/specs/vocabulary.md
View file

0
doc/workflow.md → doc/specs/workflow.md
View file

3
justfile
View file

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

14
server/Cargo.toml
View file

@ -1,14 +0,0 @@
[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"

147
server/src/main.rs
View file

@ -1,147 +0,0 @@
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));
}
}

49
store/src/board.rs
View file

@ -639,6 +639,55 @@ impl Board {
self.positions[field - 1] += unit; self.positions[field - 1] += unit;
Ok(()) Ok(())
} }
pub fn from_gnupg_pos_id(bits: &str) -> Result<Board, String> {
let mut positions = [0i8; 24];
let mut bit_idx = 0;
let bit_chars: Vec<char> = bits.chars().collect();
// White checkers (points 1 to 24)
for i in 0..24 {
if bit_idx >= bit_chars.len() {
break;
}
let mut count = 0;
while bit_idx < bit_chars.len() && bit_chars[bit_idx] == '1' {
count += 1;
bit_idx += 1;
}
positions[i] = count;
if bit_idx < bit_chars.len() && bit_chars[bit_idx] == '0' {
bit_idx += 1; // Consume the '0' separator
}
}
// Black checkers (points 24 down to 1)
for i in (0..24).rev() {
if bit_idx >= bit_chars.len() {
break;
}
let mut count = 0;
while bit_idx < bit_chars.len() && bit_chars[bit_idx] == '1' {
count += 1;
bit_idx += 1;
}
if positions[i] == 0 {
positions[i] = -count;
} else if count > 0 {
return Err(format!(
"Invalid board: checkers of both colors on point {}",
i + 1
));
}
if bit_idx < bit_chars.len() && bit_chars[bit_idx] == '0' {
bit_idx += 1; // Consume the '0' separator
}
}
Ok(Board { positions })
}
} }
// Unit Tests // Unit Tests

11
store/src/dice.rs
View file

@ -55,6 +55,17 @@ impl Dice {
format!("{:0>3b}{:0>3b}", self.values.0, self.values.1) format!("{:0>3b}{:0>3b}", self.values.0, self.values.1)
} }
pub fn from_bits_string(bits: &str) -> Result<Self, String> {
if bits.len() != 6 {
return Err("Invalid bit string length for dice".to_string());
}
let d1_str = &bits[0..3];
let d2_str = &bits[3..6];
let d1 = u8::from_str_radix(d1_str, 2).map_err(|e| e.to_string())?;
let d2 = u8::from_str_radix(d2_str, 2).map_err(|e| e.to_string())?;
Ok(Dice { values: (d1, d2) })
}
pub fn to_display_string(self) -> String { pub fn to_display_string(self) -> String {
format!("{} & {}", self.values.0, self.values.1) format!("{} & {}", self.values.0, self.values.1)
} }

102
store/src/game.rs
View file

@ -9,12 +9,13 @@ use log::{debug, error};
// use itertools::Itertools; // use itertools::Itertools;
use serde::{Deserialize, Serialize}; use serde::{Deserialize, Serialize};
use std::collections::HashMap; use std::collections::HashMap;
use std::hash::{Hash, Hasher};
use std::{fmt, str}; use std::{fmt, str};
use base64::{engine::general_purpose, Engine as _}; use base64::{engine::general_purpose, Engine as _};
/// The different stages a game can be in. (not to be confused with the entire "GameState") /// The different stages a game can be in. (not to be confused with the entire "GameState")
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)] #[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum Stage { pub enum Stage {
PreGame, PreGame,
InGame, InGame,
@ -22,7 +23,7 @@ pub enum Stage {
} }
/// The different stages a game turn can be in. /// The different stages a game turn can be in.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)] #[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TurnStage { pub enum TurnStage {
RollDice, RollDice,
RollWaiting, RollWaiting,
@ -114,6 +115,11 @@ impl Default for GameState {
} }
} }
} }
impl Hash for GameState {
fn hash<H: Hasher>(&self, state: &mut H) {
self.to_string_id().hash(state);
}
}
impl GameState { impl GameState {
/// Create a new default game /// Create a new default game
@ -253,7 +259,7 @@ impl GameState {
pos_bits.push_str(&white_bits); pos_bits.push_str(&white_bits);
pos_bits.push_str(&black_bits); pos_bits.push_str(&black_bits);
pos_bits = format!("{pos_bits:0>108}"); pos_bits = format!("{pos_bits:0<108}");
// println!("{}", pos_bits); // println!("{}", pos_bits);
let pos_u8 = pos_bits let pos_u8 = pos_bits
.as_bytes() .as_bytes()
@ -264,6 +270,81 @@ impl GameState {
general_purpose::STANDARD.encode(pos_u8) general_purpose::STANDARD.encode(pos_u8)
} }
pub fn from_string_id(id: &str) -> Result<Self, String> {
let bytes = general_purpose::STANDARD
.decode(id)
.map_err(|e| e.to_string())?;
let bits_str: String = bytes.iter().map(|byte| format!("{:06b}", byte)).collect();
// The original string was padded to 108 bits.
let bits = if bits_str.len() >= 108 {
&bits_str[..108]
} else {
return Err("Invalid decoded string length".to_string());
};
let board_bits = &bits[0..77];
let board = Board::from_gnupg_pos_id(board_bits)?;
let active_player_bit = bits.chars().nth(77).unwrap();
let active_player_color = if active_player_bit == '1' {
Color::Black
} else {
Color::White
};
let turn_stage_bits = &bits[78..81];
let turn_stage = match turn_stage_bits {
"000" => TurnStage::RollWaiting,
"001" => TurnStage::RollDice,
"010" => TurnStage::MarkPoints,
"011" => TurnStage::HoldOrGoChoice,
"100" => TurnStage::Move,
"101" => TurnStage::MarkAdvPoints,
_ => return Err(format!("Invalid bits for turn stage : {turn_stage_bits}")),
};
let dice_bits = &bits[81..87];
let dice = Dice::from_bits_string(dice_bits).map_err(|e| e.to_string())?;
let white_player_bits = &bits[87..97];
let black_player_bits = &bits[97..107];
let white_player =
Player::from_bits_string(white_player_bits, "Player 1".to_string(), Color::White)
.map_err(|e| e.to_string())?;
let black_player =
Player::from_bits_string(black_player_bits, "Player 2".to_string(), Color::Black)
.map_err(|e| e.to_string())?;
let mut players = HashMap::new();
players.insert(1, white_player);
players.insert(2, black_player);
let active_player_id = if active_player_color == Color::White {
1
} else {
2
};
// Some fields are not in the ID, so we use defaults.
Ok(GameState {
stage: Stage::InGame, // Assume InGame from ID
turn_stage,
board,
active_player_id,
players,
history: Vec::new(),
dice,
dice_points: (0, 0),
dice_moves: (CheckerMove::default(), CheckerMove::default()),
dice_jans: PossibleJans::default(),
roll_first: false, // Assume not first roll
schools_enabled: false, // Assume disabled
})
}
pub fn who_plays(&self) -> Option<&Player> { pub fn who_plays(&self) -> Option<&Player> {
self.get_active_player() self.get_active_player()
} }
@ -661,6 +742,10 @@ 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 { fn mark_points(&mut self, player_id: PlayerId, points: u8) -> bool {
// Update player points and holes // Update player points and holes
let mut new_hole = false; let mut new_hole = false;
@ -850,7 +935,16 @@ mod tests {
let state = init_test_gamestate(TurnStage::RollDice); let state = init_test_gamestate(TurnStage::RollDice);
let string_id = state.to_string_id(); let string_id = state.to_string_id();
// println!("string_id : {}", string_id); // println!("string_id : {}", string_id);
assert_eq!(string_id, "Hz88AAAAAz8/IAAAAAQAADAD"); assert_eq!(string_id, "Pz84AAAABz8/AAAAAAgAASAG");
let new_state = GameState::from_string_id(&string_id).unwrap();
assert_eq!(state.board, new_state.board);
assert_eq!(state.active_player_id, new_state.active_player_id);
assert_eq!(state.turn_stage, new_state.turn_stage);
assert_eq!(state.dice, new_state.dice);
assert_eq!(
state.get_white_player().unwrap().points,
new_state.get_white_player().unwrap().points
);
} }
#[test] #[test]

20
store/src/player.rs
View file

@ -53,6 +53,26 @@ impl Player {
) )
} }
pub fn from_bits_string(bits: &str, name: String, color: Color) -> Result<Self, String> {
if bits.len() != 10 {
return Err("Invalid bit string length for player".to_string());
}
let points = u8::from_str_radix(&bits[0..4], 2).map_err(|e| e.to_string())?;
let holes = u8::from_str_radix(&bits[4..8], 2).map_err(|e| e.to_string())?;
let can_bredouille = bits.chars().nth(8).unwrap() == '1';
let can_big_bredouille = bits.chars().nth(9).unwrap() == '1';
Ok(Player {
name,
color,
points,
holes,
can_bredouille,
can_big_bredouille,
dice_roll_count: 0, // This info is not in the string id
})
}
pub fn to_vec(&self) -> Vec<u8> { pub fn to_vec(&self) -> Vec<u8> {
vec![ vec![
self.points, self.points,