79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
import numpy as np
|
|
import utils
|
|
from collections import deque
|
|
from tianshou.core.mcts.mcts import MCTS
|
|
|
|
|
|
class GoEnv:
|
|
def __init__(self, size=19, komi=6.5):
|
|
self.size = size
|
|
self.komi = 6.5
|
|
self.board = [utils.EMPTY] * (self.size * self.size)
|
|
self.history = deque(maxlen=8)
|
|
|
|
def _flatten(self, vertex):
|
|
x, y = vertex
|
|
return (x - 1) * self.size + (y - 1)
|
|
|
|
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
|
|
self.history.append(self.board)
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
def step_forward(self, state, action):
|
|
# print(state)
|
|
if state[0, 0, 0, -1] == 1:
|
|
color = 1
|
|
else:
|
|
color = -1
|
|
if action == 361:
|
|
vertex = (0, 0)
|
|
else:
|
|
vertex = (action / 19 + 1, action % 19)
|
|
self.do_move(color, vertex)
|
|
new_state = np.concatenate(
|
|
[state[:, :, :, 1:8], (np.array(self.board) == 1).reshape(1, 19, 19, 1),
|
|
state[:, :, :, 9:16], (np.array(self.board) == -1).reshape(1, 19, 19, 1),
|
|
np.array(1 - state[:, :, :, -1]).reshape(1, 19, 19, 1)],
|
|
axis=3)
|
|
return new_state, 0
|
|
|
|
|
|
class strategy(object):
|
|
def __init__(self, evaluator):
|
|
self.simulator = GoEnv()
|
|
self.evaluator = evaluator
|
|
|
|
def data_process(self, history, color):
|
|
state = np.zeros([1, 19, 19, 17])
|
|
for i in range(8):
|
|
state[0, :, :, i] = history[i] == 1
|
|
state[0, :, :, i + 8] = history[i] == -1
|
|
if color == 1:
|
|
state[0, :, :, 16] = np.ones([19, 19])
|
|
if color == -1:
|
|
state[0, :, :, 16] = np.zeros([19, 19])
|
|
return state
|
|
|
|
def gen_move(self, history, color):
|
|
self.simulator.history = history
|
|
self.simulator.board = history[-1]
|
|
state = self.data_process(history, color)
|
|
prior = self.evaluator(state)[0]
|
|
mcts = MCTS(self.simulator, self.evaluator, state, 362, prior, inverse=True, max_step=20)
|
|
temp = 1
|
|
p = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
|
|
choice = np.random.choice(362, 1, p=p).tolist()[0]
|
|
if choice == 361:
|
|
move = (0, 0)
|
|
else:
|
|
move = (choice / 19 + 1, choice % 19 + 1)
|
|
return move
|