105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
# -*- coding: utf-8 -*-
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# vim:fenc=utf-8
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# $File: game.py
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# $Date: Fri Dec 01 01:3738 2017 +0800
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# $Author: renyong15 © <mails.tsinghua.edu.cn>
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#
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from __future__ import print_function
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import utils
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import copy
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import tensorflow as tf
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import numpy as np
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import sys, os
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import go
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import model
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from collections import deque
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sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir))
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from tianshou.core.mcts.mcts import MCTS
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class Game:
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'''
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Load the real game and trained weights.
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TODO : Maybe merge with the engine class in future,
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currently leave it untouched for interacting with Go UI.
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'''
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def __init__(self, size=9, komi=3.75, checkpoint_path=None):
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self.size = size
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self.komi = komi
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self.board = [utils.EMPTY] * (self.size ** 2)
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self.history = []
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self.latest_boards = deque(maxlen=8)
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for _ in range(8):
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self.latest_boards.append(self.board)
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self.evaluator = model.ResNet(self.size, self.size**2 + 1, history_length=8)
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# self.evaluator = lambda state: self.sess.run([tf.nn.softmax(self.net.p), self.net.v],
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# feed_dict={self.net.x: state, self.net.is_training: False})
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self.game_engine = go.Go(size=self.size, komi=self.komi)
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def clear(self):
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self.board = [utils.EMPTY] * (self.size ** 2)
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self.history = []
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for _ in range(8):
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self.latest_boards.append(self.board)
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def set_size(self, n):
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self.size = n
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self.clear()
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def set_komi(self, k):
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self.komi = k
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def think(self, latest_boards, color):
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mcts = MCTS(self.game_engine, self.evaluator, [latest_boards, color], self.size ** 2 + 1, inverse=True)
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mcts.search(max_step=20)
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temp = 1
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prob = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
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choice = np.random.choice(self.size ** 2 + 1, 1, p=prob).tolist()[0]
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if choice == self.size ** 2:
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move = utils.PASS
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else:
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move = self.game_engine._deflatten(choice)
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return move, prob
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def play_move(self, color, vertex):
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# this function can be called directly to play the opponent's move
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if vertex == utils.PASS:
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return True
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res = self.game_engine.executor_do_move(self.history, self.latest_boards, self.board, color, vertex)
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return res
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def think_play_move(self, color):
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# although we don't need to return self.prob, however it is needed for neural network training
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move, self.prob = self.think(self.latest_boards, color)
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# play the move immediately
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self.play_move(color, move)
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return move
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def status2symbol(self, s):
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pool = {utils.WHITE: 'O', utils.EMPTY: '.', utils.BLACK: 'X', utils.FILL: 'F', utils.UNKNOWN: '?'}
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return pool[s]
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def show_board(self):
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row = [i for i in range(1, 20)]
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col = ' abcdefghijklmnopqrstuvwxyz'
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print(' ', end='')
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for j in range(self.size + 1):
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print(col[j], end=' ')
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print('')
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for i in range(self.size):
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print(row[i], end=' ')
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if row[i] < 10:
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print(' ', end='')
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for j in range(self.size):
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print(self.status2symbol(self.board[self._flatten((j + 1, i + 1))]), end=' ')
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print('')
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sys.stdout.flush()
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if __name__ == "__main__":
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g = Game()
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g.show_board()
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g.think_play_move(1)
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#file = open("debug.txt", "a")
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#file.write("mcts check\n")
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#file.close()
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