mctspy : python implementation of Monte Carlo Tree Search algorithm

Basic python implementation of Monte Carlo Tree Search (MCTS) intended to run on small game trees.

Installation

pip3 install mctspy

Running tic-tac-toe example

to run tic-tac-toe example:

import numpy as np
from mctspy.tree.nodes import TwoPlayersGameMonteCarloTreeSearchNode
from mctspy.tree.search import MonteCarloTreeSearch
from mctspy.games.examples.tictactoe import TicTacToeGameState

state = np.zeros((3,3))
initial_board_state = TicTacToeGameState(state = state, next_to_move=1)

root = TwoPlayersGameMonteCarloTreeSearchNode(state = initial_board_state)
mcts = MonteCarloTreeSearch(root)
best_node = mcts.best_action(10000)

Running MCTS for your own 2 players zero-sum game

If you want to apply MCTS for your own game, its state implementation should derive from
mmctspy.games.common.TwoPlayersGameState

(lookup mctspy.games.examples.tictactoe.TicTacToeGameState for inspiration)