class PolicyNetworkBestMovePlayer(GtpInterface):
def __init__(self, read_file):
self.policy_network = PolicyNetwork(DEFAULT_FEATURES.planes,
use_cpu=True)
self.read_file = read_file
super().__init__()
def clear(self):
super().clear()
self.refresh_network()
def refresh_network(self):
# Ensure that the player is using the latest version of the network
# so that the network can be continually trained even as it's playing.
self.policy_network.initialize_variables(self.read_file)
def suggest_move(self, position):
if position.recent and position.n > 100 and position.recent[-1] == None:
# Pass if the opponent passes
return None
move_probabilities = self.policy_network.run(position)
for move in sorted_moves(move_probabilities):
if go.is_reasonable(position, move):
return move
return None
tf.Variable(v,
name=name.replace('PolicNetwork',
'PlayerNetwork')))
saver = tf.train.Saver(new_vars)
sess.run(tf.global_variables_initializer())
saver.save(sess,
os.path.join(save_dir, str(t), 'player' + str(t) + '.ckpt'))
g1 = tf.Graph()
with g1.as_default():
train_net = PolicyNetwork(scope="PolicNetwork")
train_net.initialize_variables('model/sl/epoch_48.ckpt')
pos = go.Position()
train_net.run(pos)
g2 = tf.Graph()
with g2.as_default():
player_net = PolicyNetwork(scope="PlayerNetwork")
player_net.initialize_variables('model/rl/2/player2.ckpt')
pos = go.Position()
player_net.run(pos)
save_trained_policy(1, 'model/rl')
print("===========load new model=================")
g2 = tf.Graph()
with g2.as_default():
player_net = PolicyNetwork(scope="PlayerNetwork")
player_net.initialize_variables('model/rl/5/player5.ckpt')
class MCTS(GtpInterface):
def __init__(self, read_file, seconds_per_move=5):
self.seconds_per_move = seconds_per_move
self.max_rollout_depth = go.N * go.N * 3
self.policy_network = PolicyNetwork(DEFAULT_FEATURES.planes,
use_cpu=True)
self.read_file = read_file
super().__init__()
def clear(self):
super().clear()
self.refresh_network()
def refresh_network(self):
# Ensure that the player is using the latest version of the network
# so that the network can be continually trained even as it's playing.
self.policy_network.initialize_variables(self.read_file)
def suggest_move(self, position):
if position.caps[0] + 50 < position.caps[1]:
return gtp.RESIGN
start = time.time()
move_probs = self.policy_network.run(position)
root = MCTSNode.root_node(position, move_probs)
while time.time() - start < self.seconds_per_move:
self.tree_search(root)
# there's a theoretical bug here: if you refuse to pass, this AI will
# eventually start filling in its own eyes.
return max(root.children.keys(),
key=lambda move, root=root: root.children[move].N)
def tree_search(self, root):
print("tree search", file=sys.stderr)
# selection
chosen_leaf = root.select_leaf()
# expansion
position = chosen_leaf.compute_position()
if position is None:
print("illegal move!", file=sys.stderr)
# See go.Position.play_move for notes on detecting legality
del chosen_leaf.parent.children[chosen_leaf.move]
return
print("Investigating following position:\n%s" %
(chosen_leaf.position, ),
file=sys.stderr)
move_probs = self.policy_network.run(position)
chosen_leaf.expand(move_probs)
# evaluation
value = self.estimate_value(chosen_leaf)
# backup
print("value: %s" % value, file=sys.stderr)
chosen_leaf.backup_value(value)
def estimate_value(self, chosen_leaf):
# Estimate value of position using rollout only (for now).
# (TODO: Value network; average the value estimations from rollout + value network)
leaf_position = chosen_leaf.position
current = leaf_position
while current.n < self.max_rollout_depth:
move_probs = self.policy_network.run(current)
current = self.play_valid_move(current, move_probs)
if len(current.recent
) > 2 and current.recent[-1] == current.recent[-2] == None:
break
else:
print("max rollout depth exceeded!", file=sys.stderr)
perspective = 1 if leaf_position.player1turn else -1
return current.score() * perspective
def play_valid_move(self, position, move_probs):
for move in sorted_moves(move_probs):
if go.is_eyeish(position.board, move):
continue
candidate_pos = position.play_move(move, mutate=True)
if candidate_pos is not None:
return candidate_pos
return position.pass_move(mutate=True)
