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chore(refact): move training_common from bot to store crate

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This commit is contained in:
Henri Bourcereau 2026-02-08 18:19:42 +01:00
parent 257665c546
commit 453c363334
12 changed files with 38 additions and 39 deletions
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2
Cargo.lock generated
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@ -6621,7 +6621,7 @@ dependencies = [
"log",
"merge",
"pyo3",
"rand 0.8.5",
"rand 0.9.2",
"serde",
"transpose",
]

1
bot/python/test.py
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@ -2,3 +2,4 @@ import trictrac_store
game = trictrac_store.TricTrac()
print(game.get_active_player_id())
print(game.get_legal_actions())

2
bot/src/burnrl/environment.rs
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@ -1,9 +1,9 @@
use std::io::Write;
use crate::training_common;
use burn::{prelude::Backend, tensor::Tensor};
use burn_rl::base::{Action, Environment, Snapshot, State};
use rand::{rng, Rng};
use trictrac_store::training_common;
use trictrac_store::{GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage};
const ERROR_REWARD: f32 = -1.0012121;

2
bot/src/burnrl/environment_valid.rs
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@ -1,7 +1,7 @@
use crate::training_common;
use burn::{prelude::Backend, tensor::Tensor};
use burn_rl::base::{Action, Environment, Snapshot, State};
use rand::{rng, Rng};
use trictrac_store::training_common;
use trictrac_store::{GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage};
const ERROR_REWARD: f32 = -1.0012121;

5
bot/src/lib.rs
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@ -1,15 +1,16 @@
pub mod burnrl;
pub mod strategy;
pub mod training_common;
pub mod trictrac_board;
use log::debug;
use trictrac_store::{CheckerMove, Color, GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage};
pub use strategy::default::DefaultStrategy;
pub use strategy::dqnburn::DqnBurnStrategy;
pub use strategy::erroneous_moves::ErroneousStrategy;
pub use strategy::random::RandomStrategy;
pub use strategy::stable_baselines3::StableBaselines3Strategy;
use trictrac_store::{
CheckerMove, Color, GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage,
};
pub trait BotStrategy: std::fmt::Debug {
fn get_game(&self) -> &GameState;

4
bot/src/strategy/dqnburn.rs
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@ -8,7 +8,9 @@ use trictrac_store::MoveRules;
use crate::burnrl::algos::dqn;
use crate::burnrl::environment;
use crate::training_common::{get_valid_action_indices, sample_valid_action, TrictracAction};
use trictrac_store::training_common::{
get_valid_action_indices, sample_valid_action, TrictracAction,
};
type DqnBurnNetwork = dqn::Net<NdArray<ElemType>>;

2
bot/src/trictrac_board.rs
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@ -1,5 +1,4 @@
// https://docs.rs/board-game/ implementation
use crate::training_common::{get_valid_actions, TrictracAction};
use board_game::board::{
Board as BoardGameBoard, BoardDone, BoardMoves, Outcome, PlayError, Player as BoardGamePlayer,
};
@ -8,6 +7,7 @@ use internal_iterator::InternalIterator;
use std::fmt;
use std::hash::Hash;
use std::ops::ControlFlow;
use trictrac_store::training_common::{get_valid_actions, TrictracAction};
use trictrac_store::Color;
#[derive(Clone, Debug, Eq, PartialEq, Hash)]

2
store/Cargo.toml
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@ -18,6 +18,6 @@ log = "0.4.20"
merge = "0.1.0"
# generate python lib (with maturin) to be used in AI training
pyo3 = { version = "0.23", features = ["extension-module", "abi3-py38"] }
rand = "0.8.5"
rand = "0.9"
serde = { version = "1.0", features = ["derive"] }
transpose = "0.2.2"

6
store/src/dice.rs
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@ -1,4 +1,4 @@
use rand::distributions::{Distribution, Uniform};
use rand::distr::{Distribution, Uniform};
use rand::{rngs::StdRng, SeedableRng};
use serde::{Deserialize, Serialize};
@ -17,7 +17,7 @@ impl DiceRoller {
pub fn new(opt_seed: Option<u64>) -> Self {
Self {
rng: match opt_seed {
None => StdRng::from_rng(rand::thread_rng()).unwrap(),
None => StdRng::from_rng(&mut rand::rng()),
Some(seed) => SeedableRng::seed_from_u64(seed),
},
}
@ -26,7 +26,7 @@ impl DiceRoller {
/// Roll the dices which generates two random numbers between 1 and 6, replicating a perfect
/// dice. We use the operating system's random number generator.
pub fn roll(&mut self) -> Dice {
let between = Uniform::new_inclusive(1, 6);
let between = Uniform::new_inclusive(1, 6).expect("1 > 6 !?");
let v = (between.sample(&mut self.rng), between.sample(&mut self.rng));

2
store/src/lib.rs
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@ -17,5 +17,7 @@ pub use board::CheckerMove;
mod dice;
pub use dice::{Dice, DiceRoller};
pub mod training_common;
// python interface "trictrac_engine" (for AI training..)
mod pyengine;

10
store/src/pyengine.rs
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@ -8,6 +8,7 @@ use crate::game::{GameEvent, GameState, Stage, TurnStage};
use crate::game_rules_moves::MoveRules;
use crate::game_rules_points::PointsRules;
use crate::player::{Color, PlayerId};
use crate::training_common::get_valid_action_indices;
#[pyclass]
struct TricTrac {
@ -49,12 +50,9 @@ impl TricTrac {
self.game_state.active_player_id - 1
}
// fn get_legal_actions(&self) -> Vec<usize> {
// get_valid_actions(&self.game_state)
// .into_iter()
// .map(|action| action.to_action_index())
// .collect()
// }
fn get_legal_actions(&self) -> Vec<usize> {
get_valid_action_indices(&self.game_state)
}
/// Lance les dés ou utilise la séquence prédéfinie
fn roll_dice(&mut self) -> PyResult<(u8, u8)> {

39
bot/src/training_common.rs → store/src/training_common.rs
View file

@ -3,8 +3,8 @@
use std::cmp::{max, min};
use std::fmt::{Debug, Display, Formatter};
use crate::{CheckerMove, GameEvent, GameState};
use serde::{Deserialize, Serialize};
use trictrac_store::{CheckerMove, GameEvent, GameState};
// 1 (Roll) + 1 (Go) + 512 (mouvements possibles)
// avec 512 = 2 (choix du dé) * 16 * 16 (choix de la dame 0-15 pour chaque from)
@ -94,14 +94,13 @@ impl TrictracAction {
(state.dice.values.1, state.dice.values.0)
};
let color = &trictrac_store::Color::White;
let color = &crate::Color::White;
let from1 = state
.board
.get_checker_field(color, *checker1 as u8)
.unwrap_or(0);
let mut to1 = from1 + dice1 as usize;
let checker_move1 =
trictrac_store::CheckerMove::new(from1, to1).unwrap_or_default();
let checker_move1 = CheckerMove::new(from1, to1).unwrap_or_default();
let mut tmp_board = state.board.clone();
let move_result = tmp_board.move_checker(color, checker_move1);
@ -121,10 +120,8 @@ impl TrictracAction {
to2 -= 1;
}
let checker_move1 =
trictrac_store::CheckerMove::new(from1, to1).unwrap_or_default();
let checker_move2 =
trictrac_store::CheckerMove::new(from2, to2).unwrap_or_default();
let checker_move1 = CheckerMove::new(from1, to1).unwrap_or_default();
let checker_move2 = CheckerMove::new(from2, to2).unwrap_or_default();
Some(GameEvent::Move {
player_id: state.active_player_id,
@ -182,8 +179,8 @@ impl TrictracAction {
// from2,
// } => {
// // Effectuer un mouvement
// let checker_move1 = trictrac_store::CheckerMove::new(move1.0, move1.1).unwrap_or_default();
// let checker_move2 = trictrac_store::CheckerMove::new(move2.0, move2.1).unwrap_or_default();
// let checker_move1 = CheckerMove::new(move1.0, move1.1).unwrap_or_default();
// let checker_move2 = CheckerMove::new(move2.0, move2.1).unwrap_or_default();
//
// Some(GameEvent::Move {
// player_id: self.agent_player_id,
@ -195,8 +192,8 @@ impl TrictracAction {
}
/// Obtient les actions valides pour l'état de jeu actuel
pub fn get_valid_actions(game_state: &crate::GameState) -> Vec<TrictracAction> {
use trictrac_store::TurnStage;
pub fn get_valid_actions(game_state: &GameState) -> Vec<TrictracAction> {
use crate::TurnStage;
let mut valid_actions = Vec::new();
@ -219,12 +216,11 @@ pub fn get_valid_actions(game_state: &crate::GameState) -> Vec<TrictracAction> {
valid_actions.push(TrictracAction::Go);
// Ajoute aussi les mouvements possibles
let rules =
trictrac_store::MoveRules::new(&color, &game_state.board, game_state.dice);
let rules = crate::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, trictrac_store::Color::White);
assert_eq!(color, crate::Color::White);
for (move1, move2) in possible_moves {
valid_actions.push(checker_moves_to_trictrac_action(
&move1, &move2, &color, game_state,
@ -232,8 +228,7 @@ pub fn get_valid_actions(game_state: &crate::GameState) -> Vec<TrictracAction> {
}
}
TurnStage::Move => {
let rules =
trictrac_store::MoveRules::new(&color, &game_state.board, game_state.dice);
let rules = crate::MoveRules::new(&color, &game_state.board, game_state.dice);
let mut possible_moves = rules.get_possible_moves_sequences(true, vec![]);
if possible_moves.is_empty() {
// Empty move
@ -241,7 +236,7 @@ pub fn get_valid_actions(game_state: &crate::GameState) -> Vec<TrictracAction> {
}
// Modififier checker_moves_to_trictrac_action si on doit gérer Black
assert_eq!(color, trictrac_store::Color::White);
assert_eq!(color, crate::Color::White);
for (move1, move2) in possible_moves {
valid_actions.push(checker_moves_to_trictrac_action(
&move1, &move2, &color, game_state,
@ -261,8 +256,8 @@ pub fn get_valid_actions(game_state: &crate::GameState) -> Vec<TrictracAction> {
fn checker_moves_to_trictrac_action(
move1: &CheckerMove,
move2: &CheckerMove,
color: &trictrac_store::Color,
state: &crate::GameState,
color: &crate::Color,
state: &GameState,
) -> TrictracAction {
let to1 = move1.get_to();
let to2 = move2.get_to();
@ -320,7 +315,7 @@ fn checker_moves_to_trictrac_action(
}
/// Retourne les indices des actions valides
pub fn get_valid_action_indices(game_state: &crate::GameState) -> Vec<usize> {
pub fn get_valid_action_indices(game_state: &GameState) -> Vec<usize> {
get_valid_actions(game_state)
.into_iter()
.map(|action| action.to_action_index())
@ -328,7 +323,7 @@ pub fn get_valid_action_indices(game_state: &crate::GameState) -> Vec<usize> {
}
/// Sélectionne une action valide aléatoire
pub fn sample_valid_action(game_state: &crate::GameState) -> Option<TrictracAction> {
pub fn sample_valid_action(game_state: &GameState) -> Option<TrictracAction> {
use rand::{prelude::IndexedRandom, rng};
let valid_actions = get_valid_actions(game_state);