def run_garage_tf(env, seed, log_dir):
"""Create garage TensorFlow VPG model and training.
Args:
env (dict): Environment of the task.
seed (int): Random positive integer for the trial.
log_dir (str): Log dir path.
Returns:
str: Path to output csv file
"""
deterministic.set_seed(seed)
with LocalTFRunner(snapshot_config) as runner:
env = TfEnv(normalize(env))
policy = TF_GMP(
env_spec=env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TF_VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
center_adv=hyper_parameters['center_adv'],
optimizer_args=dict(
tf_optimizer_args=dict(
learning_rate=hyper_parameters['learning_rate']),
verbose=True)) # yapf: disable
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
def vpg_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
with TFTrainer(ctxt) as trainer:
env = normalize(GymEnv(env_id))
policy = TF_GMP(
env_spec=env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
sampler = RaySampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
is_tf_worker=True)
algo = TF_VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=hyper_parameters['discount'],
center_adv=hyper_parameters['center_adv'],
optimizer_args=dict(
learning_rate=hyper_parameters['learning_rate'], ))
trainer.setup(algo, env)
trainer.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
def vpg_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
with LocalTFRunner(ctxt) as runner:
env = TfEnv(normalize(gym.make(env_id)))
policy = TF_GMP(
env_spec=env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TF_VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
center_adv=hyper_parameters['center_adv'],
optimizer_args=dict(tf_optimizer_args=dict(
learning_rate=hyper_parameters['learning_rate']),
verbose=True))
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])