Tianshou/AlphaGo/strategy.py

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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
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
from tianshou.core.mcts.mcts import MCTS
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DELTA = [[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:
def __init__(self, **kwargs):
self.game = kwargs['game']
self.board = [utils.EMPTY] * (self.game.size * self.game.size)
self.latest_boards = deque(maxlen=8)
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def _flatten(self, vertex):
x, y = vertex
return (x - 1) * self.game.size + (y - 1)
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def _find_group(self, start):
color = self.board[self._flatten(start)]
# print ("color : ", color)
chain = set()
frontier = [start]
has_liberty = False
while frontier:
current = frontier.pop()
# print ("current : ", current)
chain.add(current)
for n in self._neighbor(current):
# print n, self._flatten(n), self.board[self._flatten(n)],
if self.board[self._flatten(n)] == color and not n in chain:
frontier.append(n)
if self.board[self._flatten(n)] == utils.EMPTY:
has_liberty = True
return has_liberty, chain
def _bfs(self, vertex, color, block, status):
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block.append(vertex)
status[self._flatten(vertex)] = True
nei = self._neighbor(vertex)
for n in nei:
if not status[self._flatten(n)]:
if self.board[self._flatten(n)] == color:
self._bfs(n, color, block, status)
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def _is_qi(self, color, vertex):
nei = self._neighbor(vertex)
for n in nei:
if self.board[self._flatten(n)] == utils.EMPTY:
return True
self.board[self._flatten(vertex)] = color
for n in nei:
if self.board[self._flatten(n)] == utils.another_color(color):
has_liberty, group = self._find_group(n)
if not has_liberty:
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self.board[self._flatten(vertex)] = utils.EMPTY
return True
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### avoid suicide
has_liberty, group = self._find_group(vertex)
if not has_liberty:
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self.board[self._flatten(vertex)] = utils.EMPTY
return False
self.board[self._flatten(vertex)] = utils.EMPTY
return True
def _check_global_isomorphous(self, color, vertex):
##backup
_board = copy.copy(self.board)
self.board[self._flatten(vertex)] = color
self._process_board(color, vertex)
if self.board in self.latest_boards:
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res = True
else:
res = False
self.board = _board
return res
def _in_board(self, vertex):
x, y = vertex
if x < 1 or x > self.game.size: return False
if y < 1 or y > self.game.size: return False
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return True
def _neighbor(self, vertex):
x, y = vertex
nei = []
for d in DELTA:
_x = x + d[0]
_y = y + d[1]
if self._in_board((_x, _y)):
nei.append((_x, _y))
return nei
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def _corner(self, vertex):
x, y = vertex
corner = []
for d in CORNER_OFFSET:
_x = x + d[0]
_y = y + d[1]
if self._in_board((_x, _y)):
corner.append((_x, _y))
return corner
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def _process_board(self, color, vertex):
nei = self._neighbor(vertex)
for n in nei:
if self.board[self._flatten(n)] == utils.another_color(color):
has_liberty, group = self._find_group(n)
if not has_liberty:
for b in group:
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self.board[self._flatten(b)] = utils.EMPTY
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def _is_eye(self, color, vertex):
nei = self._neighbor(vertex)
cor = self._corner(vertex)
ncolor = {color == self.board[self._flatten(n)] for n in nei}
if False in ncolor:
# print "not all neighbors are in same color with us"
return False
_, group = self._find_group(nei[0])
if set(nei) < group:
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# print "all neighbors are in same group and same color with us"
return True
else:
opponent_number = [self.board[self._flatten(c)] for c in cor].count(-color)
opponent_propotion = float(opponent_number) / float(len(cor))
if opponent_propotion < 0.5:
# print "few opponents, real eye"
return True
else:
# print "many opponents, fake eye"
return False
def knowledge_prunning(self, color, vertex):
### check if it is an eye of yourself
### assumptions : notice that this judgement requires that the state is an endgame
if self._is_eye(color, vertex):
return False
return True
def simulate_is_valid(self, state, action):
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# state is the play board, the shape is [1, 9, 9, 17]
if action == self.game.size * self.game.size:
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vertex = (0, 0)
else:
vertex = (action / self.game.size + 1, action % self.game.size + 1)
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if state[0, 0, 0, -1] == utils.BLACK:
color = utils.BLACK
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else:
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color = utils.WHITE
self.latest_boards.clear()
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for i in range(8):
self.latest_boards.append((state[:, :, :, i] - state[:, :, :, i + 8]).reshape(-1).tolist())
self.board = copy.copy(self.latest_boards[-1])
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### in board
if not self._in_board(vertex):
return False
### already have stone
if not self.board[self._flatten(vertex)] == utils.EMPTY:
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# print(np.array(self.board).reshape(9, 9))
# print(vertex)
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return False
### check if it is qi
if not self._is_qi(color, vertex):
return False
### forbid global isomorphous
if self._check_global_isomorphous(color, vertex):
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return False
if not self.knowledge_prunning(color, vertex):
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return False
return True
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def do_move(self, color, vertex):
if vertex == utils.PASS:
return True
id_ = self._flatten(vertex)
if self.board[id_] == utils.EMPTY:
self.board[id_] = color
return True
else:
return False
def step_forward(self, state, action):
if state[0, 0, 0, -1] == 1:
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color = utils.BLACK
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else:
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color = utils.WHITE
if action == self.game.size ** 2:
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vertex = utils.PASS
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else:
vertex = (action % self.game.size + 1, action / self.game.size + 1)
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# print(vertex)
# print(self.board)
self.board = (state[:, :, :, 7] - state[:, :, :, 15]).reshape(-1).tolist()
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self.do_move(color, vertex)
new_state = np.concatenate(
[state[:, :, :, 1:8], (np.array(self.board) == utils.BLACK).reshape(1, self.game.size, self.game.size, 1),
state[:, :, :, 9:16], (np.array(self.board) == utils.WHITE).reshape(1, self.game.size, self.game.size, 1),
np.array(1 - state[:, :, :, -1]).reshape(1, self.game.size, self.game.size, 1)],
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axis=3)
return new_state, 0