def _init_bot(bot_type, game, player_id):
"""Initializes a bot by type."""
rng = np.random.RandomState(FLAGS.seed)
if bot_type == "mcts":
evaluator = mcts.RandomRolloutEvaluator(FLAGS.rollout_count, rng)
return mcts.MCTSBot(
game,
FLAGS.uct_c,
FLAGS.max_simulations,
evaluator,
random_state=rng,
solve=FLAGS.solve,
verbose=FLAGS.verbose)
if bot_type == "az":
model = az_model.Model.from_checkpoint(FLAGS.az_path)
evaluator = az_evaluator.AlphaZeroEvaluator(game, model)
return mcts.MCTSBot(
game,
FLAGS.uct_c,
FLAGS.max_simulations,
evaluator,
random_state=rng,
child_selection_fn=mcts.SearchNode.puct_value,
solve=FLAGS.solve,
verbose=FLAGS.verbose)
if bot_type == "random":
return uniform_random.UniformRandomBot(player_id, rng)
if bot_type == "human":
return human.HumanBot()
if bot_type == "gtp":
bot = gtp.GTPBot(game, FLAGS.gtp_path)
for cmd in FLAGS.gtp_cmd:
bot.gtp_cmd(cmd)
return bot
raise ValueError("Invalid bot type: %s" % bot_type)
def test_can_play_tic_tac_toe(self):
game = pyspiel.load_game("tic_tac_toe")
max_simulations = 100
evaluator = mcts.RandomRolloutEvaluator(n_rollouts=20)
bots = [
mcts.MCTSBot(game, UCT_C, max_simulations, evaluator),
mcts.MCTSBot(game, UCT_C, max_simulations, evaluator),
]
v = evaluate_bots.evaluate_bots(game.new_initial_state(), bots, np.random)
self.assertEqual(v[0] + v[1], 0)
def test_can_play_three_player_stochastic_games(self):
game = pyspiel.load_game("pig(players=3,winscore=20,horizon=30)")
max_simulations = 100
evaluator = mcts.RandomRolloutEvaluator(n_rollouts=5)
bots = [
mcts.MCTSBot(game, UCT_C, max_simulations, evaluator),
mcts.MCTSBot(game, UCT_C, max_simulations, evaluator),
mcts.MCTSBot(game, UCT_C, max_simulations, evaluator),
]
v = evaluate_bots.evaluate_bots(game.new_initial_state(), bots, np.random)
self.assertEqual(sum(v), 0)
def test_can_play_tic_tac_toe(self):
game = pyspiel.load_game("tic_tac_toe")
uct_c = math.sqrt(2)
max_search_nodes = 100
evaluator = mcts.RandomRolloutEvaluator(n_rollouts=20)
bots = [
mcts.MCTSBot(game, 0, uct_c, max_search_nodes, evaluator),
mcts.MCTSBot(game, 1, uct_c, max_search_nodes, evaluator),
]
v = evaluate_bots.evaluate_bots(game.new_initial_state(), bots,
np.random)
self.assertEqual(v[0] + v[1], 0)
def test_can_play_three_player_game(self):
game = pyspiel.load_game("pig(players=3,winscore=20,horizon=30)")
uct_c = math.sqrt(2)
max_search_nodes = 100
evaluator = mcts.RandomRolloutEvaluator(n_rollouts=5)
bots = [
mcts.MCTSBot(game, 0, uct_c, max_search_nodes, evaluator),
mcts.MCTSBot(game, 1, uct_c, max_search_nodes, evaluator),
mcts.MCTSBot(game, 2, uct_c, max_search_nodes, evaluator),
]
v = evaluate_bots.evaluate_bots(game.new_initial_state(), bots,
np.random)
self.assertEqual(sum(v), 0)
def test_zero_vs_mcts(policy_fn, max_search_nodes, game_name, **kwargs):
game = pyspiel.load_game(game_name)
# Alphazero first
zero_bot = AlphaZeroBot(game, 0, policy_fn=policy_fn, use_dirichlet=False, **kwargs)
mcts_bot = mcts.MCTSBot(game, 1, 1,
max_search_nodes, mcts.RandomRolloutEvaluator(1))
score1 = play_game(game, zero_bot, mcts_bot)
# Random bot first
zero_bot = AlphaZeroBot(game, 1, policy_fn=policy_fn, use_dirichlet=False, **kwargs)
mcts_bot = mcts.MCTSBot(game, 0, 1,
max_search_nodes, mcts.RandomRolloutEvaluator(1))
score2 = -play_game(game, mcts_bot, zero_bot)
return score1, score2, None
def test_net_vs_mcts(policy_fn, max_search_nodes, game_name, **kwargs):
game = pyspiel.load_game(game_name)
# Alphazero first
zero_bot = NeuralNetBot(game, 0, policy_fn)
mcts_bot = mcts.MCTSBot(game, 1, 1,
max_search_nodes, mcts.RandomRolloutEvaluator(1))
score1 = play_game(game, zero_bot, mcts_bot)
# Random bot first
zero_bot = NeuralNetBot(game, 1, policy_fn)
mcts_bot = mcts.MCTSBot(game, 0, 1,
max_search_nodes, mcts.RandomRolloutEvaluator(1))
score2 = -play_game(game, mcts_bot, zero_bot)
return score1, score2, None
def test_can_play_single_player(self):
game = pyspiel.load_game("catch")
max_simulations = 100
evaluator = mcts.RandomRolloutEvaluator(n_rollouts=20)
bots = [mcts.MCTSBot(game, UCT_C, max_simulations, evaluator)]
v = evaluate_bots.evaluate_bots(game.new_initial_state(), bots, np.random)
self.assertGreater(v[0], 0)
def new_mcts_bot(game, max_sims, evaluator):
if max_sims < 2:
raise RuntimeError('max_sims must be > 1 ... I think the implementation is broken')
return mcts.MCTSBot(
game,
uct_c=math.sqrt(2),
max_simulations=max_sims,
evaluator=evaluator)
def test_works_with_mcts(self):
game = pyspiel.load_game("tic_tac_toe")
model = build_model(game)
evaluator = evaluator_lib.AlphaZeroEvaluator(game, model)
bot = mcts.MCTSBot(
game, 1., 20, evaluator, solve=False, dirichlet_noise=(0.25, 1.))
root = bot.mcts_search(game.new_initial_state())
self.assertEqual(root.explore_count, 20)
def test_throws_on_simultaneous_game(self):
game = pyspiel.load_game("matrix_mp")
evaluator = mcts.RandomRolloutEvaluator(n_rollouts=20)
with self.assertRaises(ValueError):
mcts.MCTSBot(game,
0,
UCT_C,
max_simulations=100,
evaluator=evaluator)
def main(_):
game = pyspiel.load_game("tic_tac_toe")
# 1. Define a model
model = model_lib.Model(
FLAGS.nn_model, game.observation_tensor_shape(),
game.num_distinct_actions(), nn_width=FLAGS.nn_width,
nn_depth=FLAGS.nn_depth, weight_decay=1e-4, learning_rate=0.01, path=None)
print("Model type: {}({}, {}), size: {} variables".format(
FLAGS.nn_model, FLAGS.nn_width, FLAGS.nn_depth,
model.num_trainable_variables))
# 2. Create an MCTS bot using the model
evaluator = evaluator_lib.AlphaZeroEvaluator(game, model)
bot = mcts.MCTSBot(game,
1.,
20,
evaluator,
solve=False,
dirichlet_noise=(0.25, 1.))
# 3. Build an AlphaZero instance
a0 = alpha_zero.AlphaZero(game,
bot,
model,
replay_buffer_capacity=FLAGS.replay_buffer_capacity,
action_selection_transition=4)
# 4. Create a bot using min-max search. It can never lose tic-tac-toe, so
# a success condition for our AlphaZero bot is to draw all games with it.
minimax_bot = MinimaxBot(game)
# 5. Run training loop
for num_round in range(FLAGS.num_rounds):
logging.info("------------- Starting round %s out of %s -------------",
num_round, FLAGS.num_rounds)
if num_round % FLAGS.evaluation_frequency == 0:
num_evaluations = 50
logging.info("Playing %s games against the minimax player.",
num_evaluations)
(_, losses, draws) = bot_evaluation(game, [minimax_bot, a0.bot],
num_evaluations=50)
logging.info("Result against Minimax player: %s losses and %s draws.",
losses, draws)
logging.info("Running %s games of self play", FLAGS.num_self_play_games)
a0.self_play(num_self_play_games=FLAGS.num_self_play_games)
logging.info("Training the net for %s epochs.", FLAGS.num_training_epochs)
a0.update(FLAGS.num_training_epochs,
batch_size=FLAGS.batch_size,
verbose=True)
logging.info("Cache: %s", evaluator.cache_info())
evaluator.clear_cache()
def main(unused_argv):
uct_c = 2
game = pyspiel.load_game(FLAGS.game)
state = game.new_initial_state()
print("Initial state: ", str(state))
# Check that the games satisfies the conditions for the implemented MCTS
# algorithm
if game.num_players() not in (1, 2):
raise ValueError("Game must be a 1-player game or 2-player zero-sum game")
if (game.num_players() == 2 and
game.get_type().utility != pyspiel.GameType.Utility.ZERO_SUM):
raise ValueError("Game must be a 1-player game or 2-player zero-sum game")
# Create MCTS bot
evaluator = mcts.RandomRolloutEvaluator(FLAGS.rollout_count)
mcts_bot = mcts.MCTSBot(game, FLAGS.mcts_player, uct_c,
FLAGS.max_search_nodes, evaluator)
# Create random bot
random_bot = pyspiel.make_uniform_random_bot(game, 1 - FLAGS.mcts_player, 123)
if FLAGS.mcts_player == 0:
bots = [mcts_bot, random_bot]
else:
bots = [random_bot, mcts_bot]
while not state.is_terminal():
# The state can be three different types: chance node,
# simultaneous node, or decision node
if state.is_chance_node():
# Chance node: sample an outcome
outcomes = state.chance_outcomes()
num_actions = len(outcomes)
print("Chance node, got " + str(num_actions) + " outcomes")
action_list, prob_list = zip(*outcomes)
action = np.random.choice(action_list, p=prob_list)
print("Sampled outcome: ",
state.action_to_string(state.current_player(), action))
state.apply_action(action)
elif state.is_simultaneous_node():
raise ValueError("Game cannot have simultaneous nodes.")
else:
# Decision node: sample action for the single current player
_, action = bots[state.current_player()].step(state)
print("Player ", state.current_player(), ", randomly sampled action: ",
state.action_to_string(state.current_player(), action))
state.apply_action(action)
print("Next state: ", str(state))
# Game is now done. Print return for each player
returns = state.returns()
for pid in range(game.num_players()):
print("Return for player {} is {}".format(pid, returns[pid]))
def _init_bot(bot_type, game, player_id):
"""Initializes a bot by type."""
if bot_type == "mcts":
evaluator = mcts.RandomRolloutEvaluator(FLAGS.rollout_count)
return mcts.MCTSBot(game, player_id, FLAGS.uct_c,
FLAGS.max_search_nodes, evaluator)
if bot_type == "random":
return uniform_random.UniformRandomBot(game, player_id, np.random)
if bot_type == "human":
return human.HumanBot(game, player_id)
raise ValueError("Invalid bot type: %s" % bot_type)
def local_random_vs_mcts():
print("local_random_vs_mcts")
game = pyspiel.load_game("tic_tac_toe")
b1 = uniform_random.UniformRandomBot(0, np.random.RandomState())
b2 = mcts.MCTSBot(
game,
uct_c=math.sqrt(2),
# starts beating random bot at ~ 3 sims, 1 rollout
max_simulations=3,
evaluator=mcts.RandomRolloutEvaluator(n_rollouts=2))
print_games_per_second(game, b1, b2, time_limit_s=3)
def _init_bot(config, game, evaluator_, evaluation):
"""Initializes a bot."""
noise = None if evaluation else (config.policy_epsilon, config.policy_alpha)
return mcts.MCTSBot(
game,
config.uct_c,
config.max_simulations,
evaluator_,
solve=False,
dirichlet_noise=noise,
child_selection_fn=mcts.SearchNode.puct_value,
verbose=False)
def __init__(self, rollout_count=5, max_simulations=4000):
rng = np.random.RandomState(None)
evaluator = mcts.RandomRolloutEvaluator(rollout_count, rng)
self.game = pyspiel.load_game("geodesic_y")
self.state = self.game.new_initial_state()
self.agent = mcts.MCTSBot(self.game,
2,
max_simulations,
evaluator,
random_state=rng,
solve=True,
verbose=False)
def search_tic_tac_toe_state(initial_actions):
game = pyspiel.load_game("tic_tac_toe")
state = game.new_initial_state()
for action_str in initial_actions.split(" "):
state.apply_action(_get_action(state, action_str))
bot = mcts.MCTSBot(game,
player=state.current_player(),
uct_c=math.sqrt(2),
max_simulations=10000,
solve=True,
evaluator=mcts.RandomRolloutEvaluator(n_rollouts=20))
return bot.mcts_search(state), state
def search_tic_tac_toe_state(initial_actions):
game = pyspiel.load_game("tic_tac_toe")
state = game.new_initial_state()
for action_str in initial_actions.split(" "):
state.apply_action(_get_action(state, action_str))
rng = np.random.RandomState(42)
bot = mcts.MCTSBot(game,
UCT_C,
max_simulations=10000,
solve=True,
random_state=rng,
evaluator=mcts.RandomRolloutEvaluator(n_rollouts=20,
random_state=rng))
return bot.mcts_search(state), state
def test_mcts_vs_random_game(self):
server = self._start_game_server("tcp://*:5555")
game = NetworkGame("tcp://localhost:5555")
mcts_bot = mcts.MCTSBot(
game,
uct_c=math.sqrt(2),
max_simulations=2,
evaluator=mcts.RandomRolloutEvaluator(n_rollouts=1))
random_bot = uniform_random.UniformRandomBot(0,
np.random.RandomState())
self._play_one_game(game, mcts_bot, random_bot)
game.exit()
server.join()
def random_vs_remote_mcts():
print("random_vs_remote_mcts")
server = start_game_server("tcp://*:5555")
game = NetworkGame("tcp://localhost:5555")
random_bot = uniform_random.UniformRandomBot(0, np.random.RandomState())
mcts_bot = mcts.MCTSBot(
game,
uct_c=math.sqrt(2),
# starts beating random bot at ~ 3 sims, 1 rollout
max_simulations=3,
evaluator=mcts.RandomRolloutEvaluator(n_rollouts=2))
print_games_per_second(game, random_bot, mcts_bot, time_limit_s=3)
game.exit()
server.join()
def main():
game = NetworkGame("tcp://localhost:5555")
# bot = uniform_random.UniformRandomBot(0, np.random.RandomState())
bot = mcts.MCTSBot(game,
uct_c=math.sqrt(2),
max_simulations=3,
evaluator=mcts.RandomRolloutEvaluator(n_rollouts=2))
state = game.new_initial_state()
while not state.is_terminal():
action = bot.step(state)
print('bot action:', action)
state.apply_action(action)
game.exit()
print("done")
print(state)
def evaluator(*, game, config, logger, checkpoint, queue):
"""A process that plays the latest checkpoint vs standard MCTS."""
results = Buffer(config.evaluation_window)
logger.print("Initializing model")
# Load a new model if there's not a checkpoint, otherwise load the checkpoint.
if checkpoint is None:
model = _init_model_from_config(config)
else:
model = _init_model_from_checkpoint(checkpoint, config.path)
logger.print("Initializing bots")
az_evaluator = evaluator_lib.AlphaZeroEvaluator(game, model)
random_evaluator = mcts.RandomRolloutEvaluator()
for game_num in itertools.count():
if not update_checkpoint(logger, queue, model, az_evaluator):
return
az_player = game_num % 2
difficulty = (game_num // 2) % config.eval_levels
max_simulations = int(config.max_simulations * (10**(difficulty / 2)))
bots = [
_init_bot(config, game, az_evaluator, True),
mcts.MCTSBot(game,
config.uct_c,
max_simulations,
random_evaluator,
solve=True,
verbose=False)
]
if az_player == 1:
bots = list(reversed(bots))
trajectory = _play_game(logger,
game_num,
game,
bots,
temperature=1,
temperature_drop=0)
results.append(trajectory.returns[az_player])
queue.put((difficulty, trajectory.returns[az_player]))
logger.print("AZ: {}, MCTS: {}, AZ avg/{}: {:.3f}".format(
trajectory.returns[az_player], trajectory.returns[1 - az_player],
len(results), np.mean(results.data)))
def _init_bot(bot_type, game, player_id):
"""Initializes a bot by type."""
rng = np.random.RandomState(FLAGS.seed)
if bot_type == "mcts":
evaluator = mcts.RandomRolloutEvaluator(FLAGS.rollout_count, rng)
return mcts.MCTSBot(
game,
FLAGS.uct_c,
FLAGS.max_simulations,
evaluator,
random_state=rng,
solve=FLAGS.solve,
verbose=FLAGS.verbose)
if bot_type == "random":
return uniform_random.UniformRandomBot(player_id, rng)
if bot_type == "human":
return human.HumanBot()
raise ValueError("Invalid bot type: %s" % bot_type)
def evaluator(*, game, config, logger, num, queue):
"""A process that plays the latest checkpoint vs standard MCTS."""
max_simulations = config.max_simulations * (3**num)
logger.print("Running MCTS with", max_simulations, "simulations")
results = Buffer(config.evaluation_window)
logger.print("Initializing model")
model = _init_model_from_config(config)
logger.print("Initializing bots")
az_evaluator = evaluator_lib.AlphaZeroEvaluator(game, model)
random_evaluator = mcts.RandomRolloutEvaluator()
az_player = 0
bots = [
_init_bot(config, game, az_evaluator, True),
mcts.MCTSBot(game,
config.uct_c,
max_simulations,
random_evaluator,
solve=True,
verbose=False)
]
for game_num in itertools.count():
if not update_checkpoint(logger, queue, model, az_evaluator):
return
trajectory = _play_game(logger,
game_num,
game,
bots,
temperature=1,
temperature_drop=0)
results.append(trajectory.returns[az_player])
logger.print("AZ: {}, MCTS: {}, AZ avg/{}: {:.3f}".format(
trajectory.returns[az_player], trajectory.returns[1 - az_player],
len(results), np.mean(results.data)))
# Swap players for the next game
bots = list(reversed(bots))
az_player = 1 - az_player
def main(_):
game = pyspiel.load_game("tic_tac_toe")
num_actions = game.num_distinct_actions()
observation_shape = game.observation_tensor_shape()
# 1. Define a keras net
if FLAGS.net_type == "resnet":
net = alpha_zero.keras_resnet(
observation_shape, num_actions, num_residual_blocks=1, num_filters=256,
data_format="channels_first")
elif FLAGS.net_type == "mlp":
net = alpha_zero.keras_mlp(
observation_shape, num_actions, num_layers=2, num_hidden=64)
else:
raise ValueError(("Invalid value for 'net_type'. Must be either 'mlp' or "
"'resnet', but was %s") % FLAGS.net_type)
model = alpha_zero.Model(
net, l2_regularization=1e-4, learning_rate=0.01, device=FLAGS.device)
# 2. Create an MCTS bot using the previous keras net
evaluator = alpha_zero.AlphaZeroKerasEvaluator(game, model)
bot = mcts.MCTSBot(game,
1.,
20,
evaluator,
solve=False,
dirichlet_noise=(0.25, 1.))
# 3. Build an AlphaZero instance
a0 = alpha_zero.AlphaZero(game,
bot,
model,
replay_buffer_capacity=FLAGS.replay_buffer_capacity,
action_selection_transition=4)
# 4. Create a bot using min-max search. It can never lose tic-tac-toe, so
# a success condition for our AlphaZero bot is to draw all games with it.
minimax_bot = MinimaxBot(game)
# 5. Run training loop
for num_round in range(FLAGS.num_rounds):
logging.info("------------- Starting round %s out of %s -------------",
num_round, FLAGS.num_rounds)
if num_round % FLAGS.evaluation_frequency == 0:
num_evaluations = 50
logging.info("Playing %s games against the minimax player.",
num_evaluations)
(_, losses, draws) = bot_evaluation(game, [minimax_bot, a0.bot],
num_evaluations=50)
logging.info("Result against Minimax player: %s losses and %s draws.",
losses, draws)
logging.info("Running %s games of self play", FLAGS.num_self_play_games)
a0.self_play(num_self_play_games=FLAGS.num_self_play_games)
logging.info("Training the net for %s epochs.", FLAGS.num_training_epochs)
a0.update(FLAGS.num_training_epochs,
batch_size=FLAGS.batch_size,
verbose=True)
evaluator.value_and_prior.cache_clear()
def main(_):
game = pyspiel.load_game("tic_tac_toe")
num_actions = game.num_distinct_actions()
observation_shape = game.observation_tensor_shape()
# 1. Define a keras net
if FLAGS.net_type == "resnet":
feature_extractor = None
net = alpha_zero.keras_resnet(observation_shape,
num_actions=num_actions,
num_residual_blocks=1,
num_filters=256,
data_format="channels_first")
elif FLAGS.net_type == "mlp":
# The full length-27 observation_tensor could be trained on. But this
# demonstrates the use of a custom feature extractor, and the training
# can be faster with this smaller feature representation.
feature_extractor = mlp_feature_extractor
net = alpha_zero.keras_mlp(9, num_actions, num_layers=2, num_hidden=64)
else:
raise ValueError(
("Invalid value for 'net_type'. Must be either 'mlp' or "
"'resnet', but was %s") % FLAGS.net_type)
# 2. Create an MCTS bot using the previous keras net
evaluator = alpha_zero.AlphaZeroKerasEvaluator(
net,
optimizer=tf.train.AdamOptimizer(learning_rate=0.01),
device=FLAGS.device,
feature_extractor=feature_extractor)
bot = mcts.MCTSBot(game,
1.,
20,
evaluator,
solve=False,
dirichlet_noise=(0.25, 1.))
# 3. Build an AlphaZero instance
a0 = alpha_zero.AlphaZero(
game,
bot,
replay_buffer_capacity=FLAGS.replay_buffer_capacity,
action_selection_transition=4)
# 4. Create a bot using min-max search. It can never lose tic-tac-toe, so
# a success condition for our AlphaZero bot is to draw all games with it.
minimax_bot = MinimaxBot(game)
# 5. Run training loop
for num_round in range(FLAGS.num_rounds):
logging.info("------------- Starting round %s out of %s -------------",
num_round, FLAGS.num_rounds)
if num_round % FLAGS.evaluation_frequency == 0:
num_evaluations = 50
logging.info("Playing %s games against the minimax player.",
num_evaluations)
(_, losses, draws) = bot_evaluation(game, [minimax_bot, a0.bot],
num_evaluations=50)
logging.info(
"Result against Minimax player: %s losses and %s draws.",
losses, draws)
logging.info("Running %s games of self play",
FLAGS.num_self_play_games)
a0.self_play(num_self_play_games=FLAGS.num_self_play_games)
logging.info("Training the net for %s epochs.",
FLAGS.num_training_epochs)
a0.update(FLAGS.num_training_epochs,
batch_size=FLAGS.batch_size,
verbose=True)
def __init__(self, idx, max_simulations=500):
super().__init__(idx)
game = pyspiel.load_game("quoridor")
self.evaluator = RandomRolloutEvaluator(2)
self.bot = mcts.MCTSBot(game, UCT_C, max_simulations, self.evaluator)