multimcts 0.1.5

Monte Carlo Tree Search for multiple teams

MultiMCTS

MultiMCTS is a Python package that implements the Monte Carlo Tree Search algorithm for board games played by any number of players. With MultiMCTS, you can create AI for any board game merely by knowing the rules -- no strategy needed!

Features

• Efficient (largely C-compiled) MCTS implementation
• Support for any number of players/teams
• Easily create AI for any board game

Installation

pip install multimcts

Game Implementation

For your game, you will need to:

• Represent the game state in code
• Identify all legal moves
• Determine if the game is over and who won

You do NOT need to:

• Have domain knowledge
• Understand any strategy
• Evaluate the favorability of a game state

Usage

To use MultiMCTS, you must define your game by subclassing GameState and implementing the required methods (see tictactoe.py for an example):

• get_current_team
• get_legal_moves
• make_move
• is_terminal
• get_reward

from multimcts import MCTS, GameState

class MyGameState(GameState):
    # your implementation here...

mcts = MCTS(max_time=1)         # Create an MCTS agent. This one will spend 1 second searching for the best move.
state = MyGameState()           # Set up a new game.
while not state.is_terminal():  # Continue until the game is over.
    print(state)                # Print the current game state (implementing GameState.__repr__ might be helpful).
    state = mcts.search(state)  # Use the best move found so far.
print(state)                    # Print the final game state.

License

MIT