merge Go and GoEnv finallygit status!
This commit is contained in:
Dong Yan
parent
0456e0c15e
commit
db40994e11
@ -183,7 +183,7 @@ class GTPEngine():
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return 'unknown player', False
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def cmd_get_score(self, args, **kwargs):
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return self._game.executor.executor_get_score(), None
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return self._game.game_engine.executor_get_score(), None
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def cmd_show_board(self, args, **kwargs):
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return self._game.board, True
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@ -9,16 +9,13 @@ 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
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import sys, os
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import go
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import network_small
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import strategy
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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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import Network
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#from strategy import strategy
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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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@ -34,15 +31,11 @@ class Game:
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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.executor = go.Go(game=self)
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#self.strategy = strategy(checkpoint_path)
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self.simulator = strategy.GoEnv(game=self)
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self.net = network_small.Network()
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self.sess = self.net.forward(checkpoint_path)
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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(game=self)
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def _flatten(self, vertex):
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x, y = vertex
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@ -79,10 +72,10 @@ class Game:
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def think(self, latest_boards, color):
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# TODO : using copy is right, or should we change to deepcopy?
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self.simulator.simulate_latest_boards = copy.copy(latest_boards)
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self.simulator.simulate_board = copy.copy(latest_boards[-1])
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nn_input = self.generate_nn_input(self.simulator.simulate_latest_boards, color)
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mcts = MCTS(self.simulator, self.evaluator, nn_input, self.size ** 2 + 1, inverse=True, max_step=1)
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self.game_engine.simulate_latest_boards = copy.copy(latest_boards)
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self.game_engine.simulate_board = copy.copy(latest_boards[-1])
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nn_input = self.generate_nn_input(self.game_engine.simulate_latest_boards, color)
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mcts = MCTS(self.game_engine, self.evaluator, nn_input, self.size ** 2 + 1, inverse=True, max_step=1)
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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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@ -96,7 +89,7 @@ class Game:
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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.executor.executor_do_move(color, vertex)
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res = self.game_engine.executor_do_move(color, vertex)
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return res
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def think_play_move(self, color):
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@ -1,7 +1,7 @@
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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 sys
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import numpy as np
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from collections import deque
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'''
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@ -12,10 +12,13 @@ Settings of the Go game.
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'''
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NEIGHBOR_OFFSET = [[1, 0], [-1, 0], [0, -1], [0, 1]]
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CORNER_OFFSET = [[-1, -1], [-1, 1], [1, 1], [1, -1]]
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class Go:
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def __init__(self, **kwargs):
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self.game = kwargs['game']
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self.simulate_board = [utils.EMPTY] * (self.game.size ** 2)
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self.simulate_latest_boards = deque(maxlen=8)
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def _in_board(self, vertex):
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x, y = vertex
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@ -33,6 +36,16 @@ class Go:
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nei.append((_x, _y))
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return nei
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def _corner(self, vertex):
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x, y = vertex
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corner = []
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for d in CORNER_OFFSET:
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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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corner.append((_x, _y))
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return corner
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def _find_group(self, current_board, vertex):
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color = current_board[self.game._flatten(vertex)]
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# print ("color : ", color)
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@ -84,6 +97,47 @@ class Go:
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repeat = True
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return repeat
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def _is_eye(self, current_board, color, vertex):
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nei = self._neighbor(vertex)
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cor = self._corner(vertex)
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ncolor = {color == current_board[self.game._flatten(n)] for n in nei}
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if False in ncolor:
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# print "not all neighbors are in same color with us"
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return False
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_, group = self._find_group(current_board, nei[0])
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if set(nei) < group:
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# print "all neighbors are in same group and same color with us"
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return True
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else:
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opponent_number = [current_board[self.game._flatten(c)] for c in cor].count(-color)
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opponent_propotion = float(opponent_number) / float(len(cor))
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if opponent_propotion < 0.5:
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# print "few opponents, real eye"
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return True
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else:
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# print "many opponents, fake eye"
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return False
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def _knowledge_prunning(self, current_board, color, vertex):
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### check if it is an eye of yourself
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### assumptions : notice that this judgement requires that the state is an endgame
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if self._is_eye(current_board, color, vertex):
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return False
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return True
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def _sa2cv(self, state, action):
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# State is the play board, the shape is [1, self.game.size, self.game.size, 17], action is an index.
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# We need to transfer the (state, action) pair into (color, vertex) pair to simulate the move
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if state[0, 0, 0, -1] == utils.BLACK:
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color = utils.BLACK
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else:
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color = utils.WHITE
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if action == self.game.size ** 2:
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vertex = (0, 0)
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else:
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vertex = self.game._deflatten(action)
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return color, vertex
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def _is_valid(self, history_boards, current_board, color, vertex):
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### in board
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if not self._in_board(vertex):
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@ -97,11 +151,54 @@ class Go:
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if self._is_suicide(current_board, color, vertex):
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return False
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### forbid global isomorphous
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if self._check_global_isomorphous(history_boards, current_board, color, vertex):
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return False
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return True
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def simulate_is_valid(self, history_boards, current_board, state, action):
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# initialize simulate_latest_boards and simulate_board from state
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self.simulate_latest_boards.clear()
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for i in range(8):
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self.simulate_latest_boards.append((state[:, :, :, i] - state[:, :, :, i + 8]).reshape(-1).tolist())
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self.simulate_board = copy.copy(self.simulate_latest_boards[-1])
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color, vertex = self._sa2cv(state, action)
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if not self._is_valid(history_boards, current_board, color, vertex):
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return False
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if not self._knowledge_prunning(current_board, 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.game._flatten(vertex)
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if self.simulate_board[id_] == utils.EMPTY:
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self.simulate_board[id_] = color
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return True
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else:
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return False
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def simulate_step_forward(self, state, action):
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# initialize the simulate_board from state
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self.simulate_board = (state[:, :, :, 7] - state[:, :, :, 15]).reshape(-1).tolist()
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color, vertex = self._sa2cv(state, action)
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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.simulate_board) == utils.BLACK).reshape(1, self.game.size, self.game.size, 1),
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state[:, :, :, 9:16], (np.array(self.simulate_board) == utils.WHITE).reshape(1, self.game.size, self.game.size, 1),
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np.array(1 - state[:, :, :, -1]).reshape(1, self.game.size, self.game.size, 1)],
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axis=3)
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return new_state, 0
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def executor_do_move(self, color, vertex):
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if not self._is_valid(self.game.history, self.game.board, color, vertex):
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return False
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@ -79,7 +79,7 @@ while True:
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prob.append(np.array(game.prob).reshape(-1, game.size ** 2 + 1))
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print("Finished")
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print("\n")
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score = game.executor.executor_get_score(True)
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score = game.game_engine.executor_get_score(True)
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if score > 0:
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winner = utils.BLACK
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else:
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@ -1,199 +0,0 @@
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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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NEIGHBOR_OFFSET = [[1, 0], [-1, 0], [0, -1], [0, 1]]
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CORNER_OFFSET = [[-1, -1], [-1, 1], [1, 1], [1, -1]]
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class GoEnv:
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def __init__(self, **kwargs):
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self.game = kwargs['game']
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self.simulate_board = [utils.EMPTY] * (self.game.size ** 2)
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self.simulate_latest_boards = deque(maxlen=8)
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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.game.size: return False
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if y < 1 or y > self.game.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 NEIGHBOR_OFFSET:
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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 _corner(self, vertex):
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x, y = vertex
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corner = []
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for d in CORNER_OFFSET:
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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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corner.append((_x, _y))
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return corner
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def _find_group(self, current_board, vertex):
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color = current_board[self.game._flatten(vertex)]
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# print ("color : ", color)
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chain = set()
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frontier = [vertex]
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has_liberty = False
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while frontier:
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current = frontier.pop()
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# print ("current : ", current)
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chain.add(current)
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for n in self._neighbor(current):
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if current_board[self.game._flatten(n)] == color and not n in chain:
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frontier.append(n)
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if current_board[self.game._flatten(n)] == utils.EMPTY:
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has_liberty = True
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return has_liberty, chain
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def _is_suicide(self, current_board, color, vertex):
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current_board[self.game._flatten(vertex)] = color # assume that we already take this move
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suicide = False
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has_liberty, group = self._find_group(current_board, vertex)
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if not has_liberty:
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suicide = True # no liberty, suicide
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for n in self._neighbor(vertex):
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if current_board[self.game._flatten(n)] == utils.another_color(color):
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opponent_liberty, group = self._find_group(current_board, n)
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if not opponent_liberty:
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suicide = False # this move is able to take opponent's stone, not suicide
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current_board[self.game._flatten(vertex)] = utils.EMPTY # undo this move
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return suicide
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def _process_board(self, current_board, color, vertex):
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nei = self._neighbor(vertex)
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for n in nei:
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if current_board[self.game._flatten(n)] == utils.another_color(color):
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has_liberty, group = self._find_group(current_board, n)
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if not has_liberty:
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for b in group:
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current_board[self.game._flatten(b)] = utils.EMPTY
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def _check_global_isomorphous(self, history_boards, current_board, color, vertex):
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repeat = False
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next_board = copy.copy(current_board)
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next_board[self.game._flatten(vertex)] = color
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self._process_board(next_board, color, vertex)
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if next_board in history_boards:
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repeat = True
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return repeat
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def _is_eye(self, current_board, color, vertex):
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nei = self._neighbor(vertex)
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cor = self._corner(vertex)
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ncolor = {color == current_board[self.game._flatten(n)] for n in nei}
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if False in ncolor:
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# print "not all neighbors are in same color with us"
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return False
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_, group = self._find_group(current_board, nei[0])
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if set(nei) < group:
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# print "all neighbors are in same group and same color with us"
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return True
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else:
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opponent_number = [current_board[self.game._flatten(c)] for c in cor].count(-color)
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opponent_propotion = float(opponent_number) / float(len(cor))
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if opponent_propotion < 0.5:
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# print "few opponents, real eye"
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return True
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else:
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# print "many opponents, fake eye"
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return False
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def _knowledge_prunning(self, current_board, color, vertex):
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### check if it is an eye of yourself
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### assumptions : notice that this judgement requires that the state is an endgame
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if self._is_eye(current_board, color, vertex):
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return False
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return True
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def _sa2cv(self, state, action):
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# State is the play board, the shape is [1, self.game.size, self.game.size, 17], action is an index.
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# We need to transfer the (state, action) pair into (color, vertex) pair to simulate the move
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if state[0, 0, 0, -1] == utils.BLACK:
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color = utils.BLACK
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else:
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color = utils.WHITE
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if action == self.game.size ** 2:
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vertex = (0, 0)
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else:
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vertex = self.game._deflatten(action)
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return color, vertex
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def _is_valid(self, history_boards, current_board, color, vertex):
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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 current_board[self.game._flatten(vertex)] == utils.EMPTY:
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return False
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### check if it is suicide
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if self._is_suicide(current_board, color, vertex):
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return False
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### forbid global isomorphous
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if self._check_global_isomorphous(history_boards, current_board, color, vertex):
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return False
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return True
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def simulate_is_valid(self, history_boards, current_board, state, action):
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# initialize simulate_latest_boards and simulate_board from state
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self.simulate_latest_boards.clear()
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for i in range(8):
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self.simulate_latest_boards.append((state[:, :, :, i] - state[:, :, :, i + 8]).reshape(-1).tolist())
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self.simulate_board = copy.copy(self.simulate_latest_boards[-1])
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color, vertex = self._sa2cv(state, action)
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if not self._is_valid(history_boards, current_board, color, vertex):
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return False
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if not self._knowledge_prunning(current_board, 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.game._flatten(vertex)
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if self.simulate_board[id_] == utils.EMPTY:
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self.simulate_board[id_] = color
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return True
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else:
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return False
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def simulate_step_forward(self, state, action):
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# initialize the simulate_board from state
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self.simulate_board = (state[:, :, :, 7] - state[:, :, :, 15]).reshape(-1).tolist()
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color, vertex = self._sa2cv(state, action)
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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.simulate_board) == utils.BLACK).reshape(1, self.game.size, self.game.size, 1),
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state[:, :, :, 9:16], (np.array(self.simulate_board) == utils.WHITE).reshape(1, self.game.size, self.game.size, 1),
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np.array(1 - state[:, :, :, -1]).reshape(1, self.game.size, self.game.size, 1)],
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axis=3)
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return new_state, 0
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