79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
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import numpy as np
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import utils
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from collections import deque
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from tianshou.core.mcts.mcts import MCTS
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class GoEnv:
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def __init__(self, size=19, komi=6.5):
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self.size = size
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self.komi = 6.5
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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 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(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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# print(state)
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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 == 361:
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vertex = (0, 0)
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else:
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vertex = (action / 19 + 1, action % 19)
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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, 19, 19, 1),
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state[:, :, :, 9:16], (np.array(self.board) == -1).reshape(1, 19, 19, 1),
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np.array(1 - state[:, :, :, -1]).reshape(1, 19, 19, 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, evaluator):
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self.simulator = GoEnv()
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self.evaluator = evaluator
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def data_process(self, history, color):
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state = np.zeros([1, 19, 19, 17])
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for i in range(8):
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state[0, :, :, i] = history[i] == 1
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state[0, :, :, i + 8] = history[i] == -1
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if color == 1:
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state[0, :, :, 16] = np.ones([19, 19])
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if color == -1:
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state[0, :, :, 16] = np.zeros([19, 19])
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return state
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def gen_move(self, history, color):
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self.simulator.history = history
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self.simulator.board = history[-1]
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state = self.data_process(history, color)
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prior = self.evaluator(state)[0]
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mcts = MCTS(self.simulator, self.evaluator, state, 362, prior, inverse=True, max_step=20)
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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(362, 1, p=p).tolist()[0]
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if choice == 361:
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move = (0, 0)
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else:
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move = (choice / 19 + 1, choice % 19 + 1)
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return move
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