class BasicPlayer(Player):
def __init__(self, game, num_playouts: int, c_puct: float=5):
self.num_playouts = num_playouts
self._mcts = MCTS(partial(self.policy_value_fn, to_dict=True), c_puct)
self._self_play = False
self._c_puct = c_puct
super(BasicPlayer, self).__init__(game)
def get_action(self, last_move, return_probs=False, temperature=0.1):
if last_move:
self._mcts.update_with_move(last_move)
action_probs = self._mcts.get_action_probs(self._game, num_playouts=self.num_playouts, temperature=temperature)
actions, probs = zip(*action_probs.items())
i = np.random.choice(np.arange(len(actions)), p=np.asarray(probs))
print(actions, probs, i)
action = actions[i]
self._mcts.update_with_move(action)
if return_probs:
full_probs = np.zeros(config.BOARD_SIZE*config.BOARD_SIZE)
rows, cols = zip(*actions)
full_probs[np.array(rows) * config.BOARD_SIZE + np.array(cols)] = np.array(probs)
return action, full_probs.ravel()
else:
return action
def policy_value_fn(self, board, to_dict=False):
value = 0
moves = available_moves(board)
p = 1 / len(moves)
prior_probs = {move: p for move in moves}
return prior_probs, value
def reset(self):
self._mcts = MCTS(partial(self.policy_value_fn, to_dict=True), self._c_puct)
class AlphaPlayer(Player):
def __init__(self, game, network: nn.Module, c_puct: float=5):
self._network = network
network.eval()
self._c_puct = c_puct
self._mcts = MCTS(partial(self.policy_value_fn, to_dict=True), c_puct)
self._self_play = False
super(AlphaPlayer, self).__init__(game)
def get_action(self, last_move, return_probs=False, temperature=0.1):
if last_move:
self._mcts.update_with_move(last_move)
action_probs = self._mcts.get_action_probs(self._game, num_playouts=config.NUM_PLAYOUTS, temperature=temperature)
actions, probs = zip(*action_probs.items())
if self._self_play:
i = np.random.choice(np.arange(len(actions)), p=np.asarray(probs)*0.75+np.random.dirichlet(0.3*np.ones(len(actions)))*0.25)
else:
i = np.random.choice(np.arange(len(actions)), p=np.asarray(probs))
action = actions[i]
self._mcts.update_with_move(action)
if return_probs:
full_probs = np.zeros(config.BOARD_SIZE*config.BOARD_SIZE)
rows, cols = zip(*actions)
full_probs[np.array(rows) * config.BOARD_SIZE + np.array(cols)] = np.array(probs)
return action, full_probs.ravel()
else:
return action
def set_self_play(self, value: bool):
self._self_play = value
def policy_value_fn(self, board, to_dict=False):
x = board_to_state(board)
x = th.tensor(x).float().to(self._network.device).unsqueeze(0)
with th.no_grad():
prior_probs, value = self._network(x)
if to_dict:
prior_probs = prior_probs.cpu().view(*board.shape).numpy()
moves = available_moves(board)
rows, cols = zip(*moves)
prior_probs = dict(zip(moves, prior_probs[np.array(rows), np.array(cols)]))
value = value[0, 0].item()
return prior_probs, value
def reset(self):
self._mcts = MCTS(partial(self.policy_value_fn, to_dict=True), self._c_puct)