def __init__(self, size, reward_method='real', black_first=True):
'''
@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.state = GoGame.get_init_board(size, black_first)
self.reward_method = RewardMethod(reward_method)
self.observation_space = gym.spaces.Box(0, 6, shape=(6, size, size))
self.action_space = gym.spaces.Discrete(
GoGame.get_action_size(self.state))
self.group_map = np.empty(self.state.shape[1:], dtype=object)
self.clear_cache()
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.get_init_board(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.get_action_size(self.state))
self.group_map = [set(), set()]
self.done = False
def __init__(self,
size,
reward_method='real',
black_first=True,
state=None):
'''
@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
if state is None:
self.state = GoGame.get_init_board(size, black_first)
else:
assert state.shape[1] == size
self.state = np.copy(state)
self.reward_method = RewardMethod(reward_method)
self.observation_space = gym.spaces.Box(0, 6, shape=(6, size, size))
self.action_space = gym.spaces.Discrete(
GoGame.get_action_size(self.state))
