refact: docs

README.md

@@ -1,7 +1,37 @@
 # 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, 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).