207 lines
6.7 KiB
Python
207 lines
6.7 KiB
Python
import os, sys
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sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir))
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import numpy as np
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import utils
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import time
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import copy
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import network_small
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import tensorflow as tf
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from collections import deque
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from tianshou.core.mcts.mcts import MCTS
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DELTA = [[1, 0], [-1, 0], [0, -1], [0, 1]]
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class GoEnv:
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def __init__(self, size=9, komi=6.5):
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self.size = size
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self.komi = komi
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self.board = [utils.EMPTY] * (self.size * self.size)
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self.history = deque(maxlen=8)
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def _flatten(self, vertex):
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x, y = vertex
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return (x - 1) * self.size + (y - 1)
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def _bfs(self, vertex, color, block, status, alive_break):
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block.append(vertex)
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status[self._flatten(vertex)] = True
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nei = self._neighbor(vertex)
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for n in nei:
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if not status[self._flatten(n)]:
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if self.board[self._flatten(n)] == color:
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self._bfs(n, color, block, status, alive_break)
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def _find_block(self, vertex, alive_break=False):
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block = []
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status = [False] * (self.size * self.size)
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color = self.board[self._flatten(vertex)]
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self._bfs(vertex, color, block, status, alive_break)
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for b in block:
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for n in self._neighbor(b):
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if self.board[self._flatten(n)] == utils.EMPTY:
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return False, block
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return True, block
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def _is_qi(self, color, vertex):
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nei = self._neighbor(vertex)
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for n in nei:
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if self.board[self._flatten(n)] == utils.EMPTY:
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return True
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self.board[self._flatten(vertex)] = color
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for n in nei:
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if self.board[self._flatten(n)] == utils.another_color(color):
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can_kill, block = self._find_block(n)
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if can_kill:
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self.board[self._flatten(vertex)] = utils.EMPTY
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return True
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### can not suicide
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can_kill, block = self._find_block(vertex)
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if can_kill:
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self.board[self._flatten(vertex)] = utils.EMPTY
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return False
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self.board[self._flatten(vertex)] = utils.EMPTY
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return True
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def _check_global_isomorphous(self, color, vertex):
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##backup
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_board = copy.copy(self.board)
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self.board[self._flatten(vertex)] = color
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self._process_board(color, vertex)
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if self.board in self.history:
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res = True
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else:
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res = False
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self.board = _board
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return res
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def _in_board(self, vertex):
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x, y = vertex
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if x < 1 or x > self.size: return False
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if y < 1 or y > self.size: return False
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return True
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def _neighbor(self, vertex):
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x, y = vertex
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nei = []
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for d in DELTA:
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_x = x + d[0]
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_y = y + d[1]
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if self._in_board((_x, _y)):
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nei.append((_x, _y))
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return nei
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def _process_board(self, color, vertex):
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nei = self._neighbor(vertex)
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for n in nei:
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if self.board[self._flatten(n)] == utils.another_color(color):
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can_kill, block = self._find_block(n, alive_break=True)
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if can_kill:
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for b in block:
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self.board[self._flatten(b)] = utils.EMPTY
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# def is_valid(self, color, vertex):
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def is_valid(self, state, action):
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if action == 81:
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vertex = (0, 0)
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else:
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vertex = (action / 9 + 1, action % 9 + 1)
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if state[0, 0, 0, -1] == 1:
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color = 1
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else:
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color = -1
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self.history.clear()
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for i in range(8):
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self.history.append((state[:, :, :, i] - state[:, :, :, i + 8]).reshape(-1).tolist())
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self.board = copy.copy(self.history[-1])
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### in board
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if not self._in_board(vertex):
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return False
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### already have stone
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if not self.board[self._flatten(vertex)] == utils.EMPTY:
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# print(np.array(self.board).reshape(9, 9))
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# print(vertex)
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return False
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### check if it is qi
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if not self._is_qi(color, vertex):
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return False
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if self._check_global_isomorphous(color, vertex):
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return False
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return True
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def do_move(self, color, vertex):
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if vertex == utils.PASS:
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return True
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id_ = self._flatten(vertex)
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if self.board[id_] == utils.EMPTY:
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self.board[id_] = color
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self.history.append(copy.copy(self.board))
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return True
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else:
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return False
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def step_forward(self, state, action):
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if state[0, 0, 0, -1] == 1:
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color = 1
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else:
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color = -1
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if action == 81:
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vertex = (0, 0)
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else:
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vertex = (action % 9 + 1, action / 9 + 1)
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# print(vertex)
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# print(self.board)
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self.board = (state[:, :, :, 7] - state[:, :, :, 15]).reshape(-1).tolist()
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self.do_move(color, vertex)
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new_state = np.concatenate(
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[state[:, :, :, 1:8], (np.array(self.board) == 1).reshape(1, 9, 9, 1),
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state[:, :, :, 9:16], (np.array(self.board) == -1).reshape(1, 9, 9, 1),
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np.array(1 - state[:, :, :, -1]).reshape(1, 9, 9, 1)],
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axis=3)
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return new_state, 0
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class strategy(object):
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def __init__(self):
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self.simulator = GoEnv()
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self.net = network_small.Network()
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self.sess = self.net.forward()
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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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def data_process(self, history, color):
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state = np.zeros([1, 9, 9, 17])
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for i in range(8):
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state[0, :, :, i] = np.array(np.array(history[i]) == np.ones(81)).reshape(9, 9)
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state[0, :, :, i + 8] = np.array(np.array(history[i]) == -np.ones(81)).reshape(9, 9)
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if color == 1:
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state[0, :, :, 16] = np.ones([9, 9])
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if color == -1:
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state[0, :, :, 16] = np.zeros([9, 9])
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return state
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def gen_move(self, history, color):
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self.simulator.history = copy.copy(history)
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self.simulator.board = copy.copy(history[-1])
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state = self.data_process(self.simulator.history, color)
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mcts = MCTS(self.simulator, self.evaluator, state, 82, inverse=True, max_step=10)
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temp = 1
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p = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
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choice = np.random.choice(82, 1, p=p).tolist()[0]
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if choice == 81:
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move = (0, 0)
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else:
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move = (choice % 9 + 1, choice / 9 + 1)
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return move
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