def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
if FLAGS.run_mode == 'actor':
actor.actor_loop(env.create_environment)
elif FLAGS.run_mode == 'learner':
learner.learner_loop(env.create_environment, create_agent,
create_optimizer)
else:
raise ValueError('Unsupported run mode {}'.format(FLAGS.run_mode))
def main(argv):
fps_log = Logger('fps.log', level='info')
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
if FLAGS.run_mode == 'actor':
actor.actor_loop(create_atari_env)
elif FLAGS.run_mode == 'learner':
learner.learner_loop(create_atari_env, create_agent, create_optimizer,
fps_log)
else:
raise ValueError('Unsupported run mode {}'.format(FLAGS.run_mode))
def main(argv):
create_environment = lambda task, config: env.create_environment(
env_name=config.env_name,
discretization=config.discretization,
n_actions_per_dim=config.n_actions_per_dim,
action_ratio=config.action_ratio)
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
if FLAGS.run_mode == 'actor':
actor.actor_loop(create_environment)
elif FLAGS.run_mode == 'learner':
learner.learner_loop(create_environment,
create_agent,
create_optimizer)
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.')
if FLAGS.run_mode == 'actor':
actor.actor_loop(env.create_environment)
elif FLAGS.run_mode == 'learner':
neptune.init('do-not-be-hasty/matrace')
neptune.create_experiment(tags=[FLAGS.nonce])
neptune_tensorboard.integrate_with_tensorflow()
learner.learner_loop(env.create_environment, create_agent,
create_optimizer)
elif FLAGS.run_mode == 'visualize':
visualize.visualize(env.create_environment, create_agent,
create_optimizer)
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.')
if FLAGS.run_mode == 'actor':
if not FLAGS.is_local:
get_configuration(config_file=FLAGS.mrunner_config,
inject_parameters_to_FLAGS=True)
actor.actor_loop(env.create_environment)
elif FLAGS.run_mode == 'learner':
if not FLAGS.is_local:
get_configuration(config_file=FLAGS.mrunner_config,
print_diagnostics=True,
with_neptune=True,
inject_parameters_to_FLAGS=True)
experiment = neptune.get_experiment()
experiment.append_tag(tag=FLAGS.nonce)
neptune_tensorboard.integrate_with_tensorflow()
learner.learner_loop(env.create_environment, create_agent,
create_optimizer)
elif FLAGS.run_mode == 'visualize':
visualize.visualize(env.create_environment, create_agent,
create_optimizer)
else:
raise ValueError('Unsupported run mode {}'.format(FLAGS.run_mode))