def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
env_descriptor = env.get_descriptor()
mzconfig = agent_lib.muzeroconfig_from_flags(env_descriptor=env_descriptor)
create_agent_fn = functools.partial(
agent_lib.create_agent,
agent_config=agent_lib.agent_config_from_flags())
if FLAGS.run_mode == 'actor':
logging.info('Make actor, %s/%s', FLAGS.task, FLAGS.num_envs)
actor.actor_loop(
functools.partial(env.create_environment,
stop_after_seeing_new_results=common_flags.
STOP_AFTER_SEEING_NEW_RESULTS.value > 0),
mzconfig)
elif FLAGS.run_mode == 'learner':
learner.learner_loop(env_descriptor=env_descriptor,
create_agent_fn=create_agent_fn,
create_optimizer_fn=create_optimizer,
config=learner_flags.learner_config_from_flags(),
mzconfig=mzconfig)
else:
raise ValueError('Unsupported run mode {}'.format(FLAGS.run_mode))
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
def visit_softmax_temperature(num_moves, training_steps, is_training=True):
if not is_training:
return 0.
if training_steps < 500e3 * 1024 / FLAGS.batch_size:
return 1. * FLAGS.temperature
elif training_steps < 750e3 * 1024 / FLAGS.batch_size:
return 0.5 * FLAGS.temperature
else:
return 0.25 * FLAGS.temperature
env_descriptor = env.get_descriptor()
known_bounds = None
mzconfig = mzcore.MuZeroConfig(
action_space_size=env_descriptor.action_space.n,
max_moves=27000,
discount=1.0 - FLAGS.one_minus_discount,
dirichlet_alpha=FLAGS.dirichlet_alpha,
root_exploration_fraction=FLAGS.root_exploration_fraction,
num_simulations=FLAGS.num_simulations,
initial_inference_batch_size=(
learner_flags.INITIAL_INFERENCE_BATCH_SIZE.value),
recurrent_inference_batch_size=(
learner_flags.RECURRENT_INFERENCE_BATCH_SIZE.value),
train_batch_size=learner_flags.BATCH_SIZE.value,
td_steps=FLAGS.td_steps,
num_unroll_steps=FLAGS.num_unroll_steps,
pb_c_base=FLAGS.pb_c_base,
pb_c_init=FLAGS.pb_c_init,
known_bounds=known_bounds,
visit_softmax_temperature_fn=visit_softmax_temperature,
use_softmax_for_action_selection=(
FLAGS.use_softmax_for_action_selection == 1))
if FLAGS.run_mode == 'actor':
actor.actor_loop(env.create_environment, mzconfig)
elif FLAGS.run_mode == 'learner':
learner.learner_loop(env_descriptor,
create_agent,
create_optimizer,
learner_flags.learner_config_from_flags(),
mzconfig,
pretraining=(FLAGS.pretraining == 1))
else:
raise ValueError('Unsupported run mode {}'.format(FLAGS.run_mode))
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
def visit_softmax_temperature(num_moves, training_steps, is_training=True):
if not is_training:
return 0.
if FLAGS.play_max_after_moves < 0:
return FLAGS.temperature
if num_moves < FLAGS.play_max_after_moves:
return FLAGS.temperature
else:
return 0.
env_descriptor = env.get_descriptor()
# Known bounds for Q-values have to include rewards and values.
known_bounds = mzcore.KnownBounds(*map(
sum, zip(env_descriptor.reward_range, env_descriptor.value_range)))
mzconfig = mzcore.MuZeroConfig(
action_space_size=env_descriptor.action_space.n,
max_moves=env_descriptor.action_space.n,
discount=1.0 - FLAGS.one_minus_discount,
dirichlet_alpha=FLAGS.dirichlet_alpha,
root_exploration_fraction=FLAGS.root_exploration_fraction,
num_simulations=FLAGS.num_simulations,
initial_inference_batch_size=(
learner_flags.INITIAL_INFERENCE_BATCH_SIZE.value),
recurrent_inference_batch_size=(
learner_flags.RECURRENT_INFERENCE_BATCH_SIZE.value),
train_batch_size=learner_flags.BATCH_SIZE.value,
td_steps=FLAGS.td_steps,
num_unroll_steps=FLAGS.num_unroll_steps,
pb_c_base=FLAGS.pb_c_base,
pb_c_init=FLAGS.pb_c_init,
known_bounds=known_bounds,
visit_softmax_temperature_fn=visit_softmax_temperature,
use_softmax_for_action_selection=(
FLAGS.use_softmax_for_action_selection == 1),
max_num_action_expansion=FLAGS.max_num_action_expansion)
if FLAGS.run_mode == 'actor':
actor.actor_loop(env.create_environment, mzconfig)
elif FLAGS.run_mode == 'learner':
learner.learner_loop(env_descriptor, create_agent, create_optimizer,
learner_flags.learner_config_from_flags(),
mzconfig)
else:
raise ValueError('Unsupported run mode {}'.format(FLAGS.run_mode))