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(unused_argv): # Save the string flags now as we modify them later. string_flags = FLAGS.flags_into_string() gin.parse_config_files_and_bindings( [FLAGS.gin_config] if FLAGS.gin_config else [], # Gin uses slashes to denote scopes but XM doesn't allow slashes in # parameter names so we use __ instead and convert it to slashes here. [s.replace('__', '/') for s in FLAGS.gin_bindings]) gym_kwargs = {} if FLAGS.mujoco_model: local_mujoco_model = tempfile.mkstemp(prefix='mujoco_model', suffix='.xml')[1] logging.info('Copying remote model %s to local file %s', FLAGS.mujoco_model, local_mujoco_model) tf.io.gfile.copy(FLAGS.mujoco_model, local_mujoco_model, overwrite=True) gym_kwargs['model_path'] = local_mujoco_model create_environment = lambda task, config: env.create_environment( env_name=config.env_name, discretization='none', n_actions_per_dim=11, action_ratio=30, gym_kwargs=gym_kwargs) if FLAGS.run_mode == 'actor': actor.actor_loop(create_environment) elif FLAGS.run_mode == 'learner': logdir = FLAGS.logdir settings = utils.init_learner_multi_host(FLAGS.num_training_tpus) learner.learner_loop( create_environment, create_agent, create_optimizer, learner_flags.training_config_from_flags(), settings, action_distribution_config=continuous_action_config()) with tf.io.gfile.GFile(os.path.join(logdir, 'learner_flags.txt'), 'w') as f: f.write(string_flags) with tf.io.gfile.GFile(os.path.join(logdir, 'learner.gin'), 'w') as f: f.write(gin.operative_config_str()) 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))