def run_metarl(env, seed, log_dir):
"""Create metarl PyTorch MAML model and training.
Args:
env (MetaRLEnv): 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)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = MAMLPPO(env=env,
policy=policy,
baseline=baseline,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
gae_lambda=hyper_parameters['gae_lambda'],
meta_batch_size=hyper_parameters['meta_batch_size'],
inner_lr=hyper_parameters['inner_lr'],
num_grad_updates=hyper_parameters['num_grad_update'])
# 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))
snapshot_config = SnapshotConfig(snapshot_dir=log_dir,
snapshot_mode='all',
snapshot_gap=1)
runner = LocalRunner(snapshot_config=snapshot_config)
runner.setup(algo, env, sampler_args=dict(n_envs=5))
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=(hyper_parameters['fast_batch_size'] *
hyper_parameters['max_path_length']))
dowel_logger.remove_all()
return tabular_log_file
def setup_method(self):
"""Setup method which is called before every test."""
self.env = MetaRLEnv(
normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.baseline = LinearFeatureBaseline(env_spec=self.env.spec)
self.algo = MAMLPPO(env=self.env,
policy=self.policy,
baseline=self.baseline,
max_path_length=100,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
def run_task(snapshot_config, *_):
"""Set up environment and algorithm and run the task.
Args:
snapshot_config (metarl.experiment.SnapshotConfig): The snapshot
configuration used by LocalRunner to create the snapshotter.
If None, it will create one with default settings.
_ : Unused parameters
"""
env = MetaRLEnv(normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
rollouts_per_task = 40
max_path_length = 100
runner = LocalRunner(snapshot_config)
algo = MAMLPPO(env=env,
policy=policy,
baseline=baseline,
max_path_length=max_path_length,
meta_batch_size=20,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
runner.setup(algo, env)
runner.train(n_epochs=300, batch_size=rollouts_per_task * max_path_length)
def test_ppo_pendulum(self):
"""Test PPO with Pendulum environment."""
deterministic.set_seed(0)
rollouts_per_task = 5
max_path_length = 100
runner = LocalRunner(snapshot_config)
algo = MAMLPPO(env=self.env,
policy=self.policy,
baseline=self.baseline,
max_path_length=max_path_length,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
runner.setup(algo, self.env)
last_avg_ret = runner.train(n_epochs=10,
batch_size=rollouts_per_task *
max_path_length)
assert last_avg_ret > -5