text: #!/usr/bin/env python3
  """This is an example to train MAML-TRPO on ML1 Push environment."""
  # pylint: disable=no-value-for-parameter
  import click
  import metaworld.benchmarks as mwb
  import torch
  
  from metarl import wrap_experiment
  from metarl.envs import MetaRLEnv, normalize
  from metarl.experiment import LocalRunner, MetaEvaluator
  from metarl.experiment.deterministic import set_seed
  from metarl.experiment.task_sampler import SetTaskSampler
  from metarl.torch.algos import MAMLTRPO
  from metarl.torch.policies import GaussianMLPPolicy
  from metarl.torch.value_functions import GaussianMLPValueFunction
  
  
  @click.command()
  @click.option('--seed', default=1)
  @click.option('--epochs', default=300)
  @click.option('--rollouts_per_task', default=10)
  @click.option('--meta_batch_size', default=20)
  @wrap_experiment(snapshot_mode='all')
  def maml_trpo_metaworld_ml1_push(ctxt, seed, epochs, rollouts_per_task,
                                   meta_batch_size):
      """Set up environment and algorithm and run the task.
  
      Args:
          ctxt (metarl.experiment.ExperimentContext): The experiment
              configuration used by LocalRunner to create the snapshotter.
          seed (int): Used to seed the random number generator to produce
              determinism.
          epochs (int): Number of training epochs.
          rollouts_per_task (int): Number of rollouts per epoch per task
              for training.
          meta_batch_size (int): Number of tasks sampled per batch.
  
      """
      set_seed(seed)
      env = MetaRLEnv(
          normalize(mwb.ML1.get_train_tasks('push-v1'),
                    expected_action_scale=10.))
  
      policy = GaussianMLPPolicy(
          env_spec=env.spec,
          hidden_sizes=(100, 100),
          hidden_nonlinearity=torch.tanh,
          output_nonlinearity=None,
      )
  
      value_function = GaussianMLPValueFunction(env_spec=env.spec,
                                                hidden_sizes=[32, 32],
                                                hidden_nonlinearity=torch.tanh,
                                                output_nonlinearity=None)
  
      max_path_length = 100
  
      test_sampler = SetTaskSampler(lambda: MetaRLEnv(
          normalize(mwb.ML1.get_test_tasks('push-v1'))))
  
      meta_evaluator = MetaEvaluator(test_task_sampler=test_sampler,
                                     max_path_length=max_path_length)
  
      runner = LocalRunner(ctxt)
      algo = MAMLTRPO(env=env,
                      policy=policy,
                      value_function=value_function,
                      max_path_length=max_path_length,
                      meta_batch_size=meta_batch_size,
                      discount=0.99,
                      gae_lambda=1.,
                      inner_lr=0.1,
                      num_grad_updates=1,
                      meta_evaluator=meta_evaluator)
  
      runner.setup(algo, env)
      runner.train(n_epochs=epochs,
                   batch_size=rollouts_per_task * max_path_length)
  
  
  maml_trpo_metaworld_ml1_push()
  