38 lines
1.6 KiB
Markdown
38 lines
1.6 KiB
Markdown
# Trictrac
|
|
|
|
This is a game of [Trictrac](https://en.wikipedia.org/wiki/Trictrac) rust implementation.
|
|
|
|
The project is on its early stages.
|
|
Rules (without "schools") are implemented, you can play in a terminal against a bot which plays randomly.
|
|
|
|
Training of AI bots is the work in progress.
|
|
|
|
## 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).
|