42 lines
1.7 KiB
Markdown
42 lines
1.7 KiB
Markdown
# Trictrac
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This is a game of [Trictrac](https://en.wikipedia.org/wiki/Trictrac) rust implementation.
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The project is on its early stages.
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Rules (without "schools") are implemented, as well as a rudimentary terminal interface which allow you to play against a bot which plays randomly.
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Training of AI bots is the work in progress.
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## Usage
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`cargo run --bin=client_cli -- --bot random`
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## Roadmap
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- [x] rules
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- [x] command line interface
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- [x] basic bot (random play)
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- [ ] AI bot
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- [ ] network game
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- [ ] web client
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## Code structure
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- game rules and game state are implemented in the _store/_ folder.
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- 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
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- the bots algorithms and the training of their models are implemented in the _bot/_ folder
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### _store_ package
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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.
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### _client_cli_ package
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`client_cli/src/game_runner.rs` contains the logic to make two bots play against each other.
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### _bot_ package
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- `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.
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- `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/>).
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- `bot/scripts/trains.sh` allows you to train agents using different algorithms (DQN, PPO, SAC).
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