PyMCT 1.0.3

A simple implement for MCTS algorithm.

PyMCT

This is a simple implement for Manto Carlo Tree Search algorithm.

Example

from cmath import inf
from random import randint, random
from PyMCT.MCT import MCTNode, MCTS
from typing import Union, List
from anytree import RenderTree

create a implement of MCTS class that has unique reward function, expand function and terminate condition.

class testMCTS(MCTS):

    def __init__(self, root: MCTNode, c: Union[int, float], max_iter: int, debug=False) -> None:
        super().__init__(root, c, max_iter, debug)

    def get_reward(self, node: MCTNode):
        node._reward = node.state

    def expand(self, node: MCTNode) -> List[MCTNode]:
        continue_chance = random()
        if continue_chance <1:
            for i in range(1,randint(1,5)):
                new_node = MCTNode(state=(self.root.depth+1)*i,parent=node)
        return node.children

    def terminate(self) -> bool:
        for node in self.root.descendants:
            if node.uct == inf:
                return False
        if self.root.height >= 3:
            return True
        else:
            return False

pass the root

test_mcts = testMCTS(root=root, c=2, debug=True, max_iter=100)

start the seacrh

test_mcts.iterate()

render the tree

print(RenderTree(test_mcts.root))

print the optimal path

test_mcts.find_optimal_path()
print(test_mcts.optimal_path)