Basic python implementation of Monte Carlo Tree Search (MCTS) intended to run on small game trees.
pip3 install mctspy 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)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)
import numpy as np from mctspy.tree.nodes import TwoPlayersGameMonteCarloTreeSearchNode from mctspy.tree.search import MonteCarloTreeSearch from mctspy.games.examples.connect4 import Connect4GameState # define inital state state = np.zeros((7, 7)) board_state = Connect4GameState( state=state, next_to_move=np.random.choice([-1, 1]), win=4) # link pieces to icons pieces = {0: " ", 1: "X", -1: "O"} # print a single row of the board def stringify(row): return " " + " | ".join(map(lambda x: pieces[int(x)], row)) + " " # display the whole board def display(board): board = board.copy().T[::-1] for row in board[:-1]: print(stringify(row)) print("-"*(len(row)*4-1)) print(stringify(board[-1])) print() display(board_state.board) # keep playing until game terminates while board_state.game_result is None: # calculate best move root = TwoPlayersGameMonteCarloTreeSearchNode(state=board_state) mcts = MonteCarloTreeSearch(root) best_node = mcts.best_action(total_simulation_seconds=1) # update board board_state = best_node.state # display board display(board_state.board) # print result print(pieces[board_state.game_result])
