gym connect 4 reinforcement learning
class ConnectX(gym.Env): def __init__(self, switch_prob=0.5): self.env = make('connectx', debug=True) self.pair = [None, 'random'] self.trainer = self.env.train(self.pair) self.switch_prob = switch_prob config = self.env.configuration self.action_space = gym.spaces.Discrete(config.columns) self.observation_space = gym.spaces.Box(low=0, high=2, shape=(config.rows,config.columns,1), dtype=np.int) def switch_side(self): self.pair = self.pair[::-1] self.trainer = self.env.train(self.pair) def switch_trainer(self): current_trainer_random = 'random' in self.pair if current_trainer_random: self.pair = [None, 'negamax'] else: self.pair = [None, 'random'] self.trainer = self.env.train(self.pair) def step(self, action): return self.trainer.step(action) def reset(self): if random.uniform(0, 1) < self.switch_prob: # switch side self.switch_side() #if random.uniform(0, 1) < self.switch_prob: # switch trainer # self.switch_trainer() return self.trainer.reset()
