def reset(self):
'''
Reset state, go_board, curr_player, prev_player_passed,
done, return state
'''
self.state_ = gogame.init_state(self.size)
self.done = False
return np.copy(self.state_)
def __init__(self, size, komi=0, reward_method='real'):
'''
@param reward_method: either 'heuristic' or 'real'
heuristic: gives # black pieces - # white pieces.
real: gives 0 for in-game move, 1 for winning, -1 for losing,
0 for draw, all from black player's perspective
'''
self.size = size
self.komi = komi
self.state_ = gogame.init_state(size)
self.reward_method = RewardMethod(reward_method)
self.observation_space = gym.spaces.Box(np.float32(0),
np.float32(govars.NUM_CHNLS),
shape=(govars.NUM_CHNLS, size,
size))
self.action_space = gym.spaces.Discrete(gogame.action_size(
self.state_))
self.done = False
def exceute_episode(self):
train_examples = []
current_player = 1
state = gogame.init_state(self.args['boardSize'])
while True:
#print("while True")
canonical_board = gogame.canonical_form(state)
self.mcts = MCTS(self.game, self.model, self.args)
root = self.mcts.run(self.model, canonical_board, to_play=1)
action_probs = [
0 for _ in range((self.args['boardSize'] *
self.args['boardSize']) + 1)
]
for k, v in root.children.items():
action_probs[k] = v.visit_count
action_probs = action_probs / np.sum(action_probs)
train_examples.append(
(canonical_board, current_player, action_probs))
action = root.select_action(temperature=1)
state = gogame.next_state(state, action, canonical=False)
current_player = -current_player
reward = gogame.winning(
state) * current_player if gogame.game_ended(state) else None
if reward is not None:
ret = []
for hist_state, hist_current_player, hist_action_probs in train_examples:
# [Board, currentPlayer, actionProbabilities, Reward]
tfBoard = np.array(
[hist_state[0], hist_state[1],
hist_state[3]]).transpose().tolist()
#ret.append(np.array([tfBoard,tfBoard, hist_action_probs, reward * ((-1) ** (hist_current_player != current_player))]))
ret.append(
(tfBoard, hist_action_probs, reward *
((-1)**(hist_current_player != current_player))))
return ret
def getInitBoard(self):
# return initial board (numpy board)
return gogame.init_state(self.size)