66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
import gym
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from gym import spaces
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import numpy as np
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class TicTacToeEnv(gym.Env):
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metadata = {'render.modes': ['human']}
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def __init__(self):
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self.action_space = spaces.Tuple((spaces.Discrete(2), spaces.Discrete(9)))
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self.observation_space = spaces.Discrete(3) # Tuple(spaces.Discrete(3), spaces.Discrete(9))
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def _step(self, action):
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done = False
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reward = 0
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p, square = action
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# p = p*2 - 1
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# check move legality
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board = self.state['board']
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proposed = board[square]
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om = self.state['on_move']
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if (proposed != 0): # wrong player, not empty
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print("illegal move ", action, ". (square occupied): ", square)
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done = True
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reward = -2 * om # player who did NOT make the illegal move
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if (p != om): # wrong player, not empty
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print("illegal move ", action, " not on move: ", p)
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done = True
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reward = -2 * om # player who did NOT make the illegal move
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else:
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board[square] = p
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self.state['on_move'] = -p
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# check game over
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for i in range(3):
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# horizontals and verticals
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if ((board[i * 3] == p and board[i * 3 + 1] == p and board[i * 3 + 2 ] == p)
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or (board[i + 0] == p and board[i + 3] == p and board[i + 6] == p)):
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reward = p
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done = True
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break
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return np.array(self.state), reward, done, {}
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def _reset(self):
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self.state = {}
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self.state['board'] = [0, 0, 0, 0, 0, 0, 0, 0, 0]
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self.state['on_move'] = 1
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return self.state
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def _render(self, mode='human', close=False):
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if close:
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return
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print("on move: " , self.state['on_move'])
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for i in range (9):
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print (self.state['board'][i], end=" ")
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print()
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def move_generator(self):
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moves = []
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for i in range (9):
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if (self.state['board'][i] == 0):
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p = self.state['on_move']
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m = [p, i]
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moves.append(m)
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return moves
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