Board Game AI - Monte Carlo Tree Search

Rust implementation of Monte Carlo Tree Search for board game AIs.

Based on the existing implementation from jbradberry/mcts.

Testing

Pick the game to test by choosing which state to initialize:

// let mut state = NimState::new(10);
let mut state = AgricolaState::new(2);

Execute the game:

cd example-games/play-game
cargo run --release

Adjusting difficulty

In example-games/play-game/src/main.rs, adjusting the iterations number will increase the number of games played by the AI before making a decision.

iterations = 10001;
best_action = UCT(arena, state.clone(), iterations);

The following is a table of iterations to time per selection:

1000 - 1 second
10000 - 20 seconds
100000 - 260 seconds

Games implemented

Nim

Agricola - Still in testing