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