Gomoku_MCTS/mcts_pure.py

# -*- coding: utf-8 -*-

import numpy as np
import copy
from operator import itemgetter
import time


def softmax(x):
    probs = np.exp(x - np.max(x))
    probs /= np.sum(probs)
    return probs

def rollout_policy_fn(board):
    """a coarse, fast version of policy_fn used in the rollout phase."""
    # rollout randomly
    action_probs = np.random.rand(len(board.availables))
    return zip(board.availables, action_probs)

# 决策价值函数
def policy_value_fn(board):
    """a function that takes in a state and outputs a list of (action, probability)
    tuples and a score for the state"""
    # return uniform probabilities and 0 score for pure MCTS
    action_probs = np.ones(len(board.availables))/len(board.availables)
    return zip(board.availables, action_probs), 0


class TreeNode(object):
    """A node in the MCTS tree. Each node keeps track of its own value Q,
    prior probability P, and its visit-count-adjusted prior score u.
    """

    def __init__(self, parent, prior_p):
        self._parent = parent
        self._children = {}  # a map from action to TreeNode
        self._n_visits = 0
        self._Q = 0
        self._u = 0
        self._P = prior_p

    def expand(self, action_priors):
        """Expand tree by creating new children.
        action_priors: a list of tuples of actions and their prior probability
            according to the policy function.
        """
        for action, prob in action_priors:
            if action not in self._children:
                self._children[action] = TreeNode(self, prob)

    def select(self, c_puct):
        """Select action among children that gives maximum action value Q
        plus bonus u(P).
        Return: A tuple of (action, next_node)
        """
        return max(self._children.items(),
                   key=lambda act_node: act_node[1].get_value(c_puct))

    def update(self, leaf_value):
        """Update node values from leaf evaluation.
        leaf_value: the value of subtree evaluation from the current player's
            perspective.
        """
        # Count visit.
        self._n_visits += 1
        # Update Q, a running average of values for all visits.
        # print("=====================================")
        # print("Before, Q: {}, visits: {}, leaf_value: {}".format(self._Q, self._n_visits,leaf_value))
        self._Q += 1.0*(leaf_value - self._Q) / self._n_visits
        # print("After, Q: {}, visits: {}, leaf_value: {}".format(self._Q, self._n_visits,leaf_value))


    def update_recursive(self, leaf_value):
        """Like a call to update(), but applied recursively for all ancestors.
        """
        # If it is not root, this node's parent should be updated first.
        if self._parent:
            self._parent.update_recursive(-leaf_value)
        self.update(leaf_value)

    def get_value(self, c_puct):
        """Calculate and return the value for this node.
        It is a combination of leaf evaluations Q, and this node's prior
        adjusted for its visit count, u.
        c_puct: a number in (0, inf) controlling the relative impact of
            value Q, and prior probability P, on this node's score.
        """
        self._u = (c_puct * self._P *
                   np.sqrt(self._parent._n_visits) / (1 + self._n_visits))
        return self._Q + self._u

    def is_leaf(self):
        """Check if leaf node (i.e. no nodes below this have been expanded).
        """
        return self._children == {}

    def is_root(self):
        return self._parent is None


class MCTS(object):
    """A simple implementation of Monte Carlo Tree Search."""

    def __init__(self, policy_value_fn, c_puct=5, n_playout=2000):
        """
        policy_value_fn: a function that takes in a board state and outputs
            a list of (action, probability) tuples and also a score in [-1, 1]
            (i.e. the expected value of the end game score from the current
            player's perspective) for the current player.
        c_puct: a number in (0, inf) that controls how quickly exploration
            converges to the maximum-value policy. A higher value means
            relying on the prior more. ??? 
        """
        self._root = TreeNode(None, 1.0)
        self._policy = policy_value_fn
        self._c_puct = c_puct
        self._n_playout = n_playout

    def _playout(self, state):
        """Run a single playout from the root to the leaf, getting a value at
        the leaf and propagating it back through its parents.
        State is modified in-place, so a copy must be provided.
        """
        node = self._root
        while(1):
            if node.is_leaf():

                break
            # Greedily select next move.
            action, node = node.select(self._c_puct)
            state.do_move(action)

        action_probs, _ = self._policy(state)
        # Check for end of game
        end, winner = state.game_end()
        if not end:
            node.expand(action_probs)
        # Evaluate the leaf node by random rollout
        leaf_value = self._evaluate_rollout(state)
        # Update value and visit count of nodes in this traversal.
        node.update_recursive(-leaf_value)

    def _evaluate_rollout(self, state, limit=1000):
        """Use the rollout policy to play until the end of the game,
        returning +1 if the current player wins, -1 if the opponent wins,
        and 0 if it is a tie.
        """
        player = state.get_current_player()
        for i in range(limit):
            end, winner = state.game_end()
            if end:
                break
            action_probs = rollout_policy_fn(state)
            max_action = max(action_probs, key=itemgetter(1))[0]
            state.do_move(max_action)
        else:
            # If no break from the loop, issue a warning.
            print("WARNING: rollout reached move limit")
        if winner == -1:  # tie
            return 0
        else:
            return 1 if winner == player else -1

    def get_move(self, state):
        """Runs all playouts sequentially and returns the most visited action.
        state: the current game state

        Return: the selected action
        """
        start_time = time.time()
        # n_playout 探索的次数
        for n in range(self._n_playout):
            state_copy = copy.deepcopy(state)
            self._playout(state_copy)

        need_time = time.time() - start_time
        
        print(f" PureMCTS sum_time: {need_time / self._n_playout }, total_simulation: {self._n_playout}")

        return max(self._root._children.items(),key=lambda act_node: act_node[1]._n_visits)[0], need_time / self._n_playout

    def update_with_move(self, last_move):
        """Step forward in the tree, keeping everything we already know
        about the subtree.
        """
        if last_move in self._root._children:
            self._root = self._root._children[last_move]
            self._root._parent = None
        else:
            self._root = TreeNode(None, 1.0)

    def get_move_probs(self, state, temp=1e-3):
        """Run all playouts sequentially and return the available actions and
        their corresponding probabilities.
        state: the current game state
        temp: temperature parameter in (0, 1] controls the level of exploration
        """

        start_time_averge = 0

        ### test multi-thread
        # lock = threading.Lock()
        # with ThreadPoolExecutor(max_workers=4) as executor:
        #     for n in range(self._n_playout):
        #         start_time = time.time()

        #         state_copy = copy.deepcopy(state)
        #         executor.submit(self._playout, state_copy, lock)
        #         start_time_averge += (time.time() - start_time)
        ### end test multi-thread

        t = time.time()
        for n in range(self._n_playout):
            start_time = time.time()

            state_copy = copy.deepcopy(state)
            self._playout(state_copy)
            start_time_averge += (time.time() - start_time)
        total_time = time.time() - t
        # print('!!time!!:', time.time() - t)

        print(f" My MCTS sum_time: {total_time}, total_simulation: {self._n_playout}")

        # calc the move probabilities based on visit counts at the root node
        act_visits = [(act, node._n_visits)
                      for act, node in self._root._children.items()]

        acts, visits = zip(*act_visits)

        act_probs = softmax(1.0 / temp * np.log(np.array(visits) + 1e-10))

        return 0, acts, act_probs, total_time

    def __str__(self):
        return "MCTS"



class MCTSPlayer(object):
    """AI player based on MCTS"""
    def __init__(self, c_puct=5, n_playout=2000):
        self.mcts = MCTS(policy_value_fn, c_puct, n_playout)

    def set_player_ind(self, p):
        self.player = p

    def reset_player(self):
        self.mcts.update_with_move(-1)

    def get_action(self, board, return_time=False):
        sensible_moves = board.availables
        if len(sensible_moves) > 0:
            move, simul_mean_time = self.mcts.get_move(board)
            self.mcts.update_with_move(-1)
            print("MCTS move:", move)
            return move, simul_mean_time
        else:
            print("WARNING: the board is full")


    def __str__(self):
        return "MCTS {}".format(self.player)
    

# 多了下面这一串代码
    
class Human_Player(object):
    def __init__(self):
        pass


    def set_player_ind(self, p):
        self.player = p


    def get_action(self, board):


        sensible_moves = board.availables
        if len(sensible_moves) > 0:
            # print(sensible_moves)
        
            move = int(input("Input the move:"))
            while (move not in sensible_moves ):
                print(sensible_moves)
                move = int(input("Input the move again:"))
            return move
        else:
            print("WARNING: the board is full")

    def __str__(self):
        return "Human {}".format(self.player)