class_docstr: Natural Policy Optimization with Task Embeddings.
  
  See https://karolhausman.github.io/pdf/hausman17nips-ws2.pdf for algorithm
  reference.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      policy (garage.tf.policies.TaskEmbeddingPolicy): Policy.
      baseline (garage.tf.baselines.Baseline): The baseline.
      sampler (garage.sampler.Sampler): Sampler.
      scope (str): Scope for identifying the algorithm.
          Must be specified if running multiple algorithms
          simultaneously, each using different environments
          and policies.
      discount (float): Discount.
      gae_lambda (float): Lambda used for generalized advantage
          estimation.
      center_adv (bool): Whether to rescale the advantages
          so that they have mean 0 and standard deviation 1.
      positive_adv (bool): Whether to shift the advantages
          so that they are always positive. When used in
          conjunction with center_adv the advantages will be
          standardized before shifting.
      fixed_horizon (bool): Whether to fix horizon.
      lr_clip_range (float): The limit on the likelihood ratio between
          policies, as in PPO.
      max_kl_step (float): The maximum KL divergence between old and new
          policies, as in TRPO.
      optimizer (object): The optimizer of the algorithm. Should be the
          optimizers in garage.tf.optimizers.
      optimizer_args (dict): The arguments of the optimizer.
      policy_ent_coeff (float): The coefficient of the policy entropy.
          Setting it to zero would mean no entropy regularization.
      encoder_ent_coeff (float): The coefficient of the policy encoder
          entropy. Setting it to zero would mean no entropy regularization.
      use_softplus_entropy (bool): Whether to estimate the softmax
          distribution of the entropy to prevent the entropy from being
          negative.
      stop_ce_gradient (bool): Whether to stop the cross entropy gradient.
      inference (garage.tf.embeddings.StochasticEncoder): A encoder
          that infers the task embedding from state trajectory.
      inference_optimizer (object): The optimizer of the inference. Should be
          an optimizer in garage.tf.optimizers.
      inference_optimizer_args (dict): The arguments of the inference
          optimizer.
      inference_ce_coeff (float): The coefficient of the cross entropy of
          task embeddings inferred from task one-hot and state trajectory.
          This is effectively the coefficient of log-prob of inference.
      name (str): The name of the algorithm.
docstr: Optimize policy.
  
  Args:
      itr (int): Iteration number.
      episodes (EpisodeBatch): Batch of episodes.
      baselines (np.ndarray): Baseline predictions.
      embed_eps (np.ndarray): Embedding episodes.
      embed_ep_infos (dict): Embedding distribution information.
docstr_tok: ["Optimize","policy",".","Args",":","itr","(","int",")",":","Iteration","number",".","episodes","(","EpisodeBatch",")",":","Batch","of","episodes",".","baselines","(","np.ndarray",")",":","Baseline","predictions",".","embed_eps","(","np.ndarray",")",":","Embedding","episodes",".","embed_ep_infos","(","dict",")",":","Embedding","distribution","information","."]
code: def _optimize_policy(
      self, itr, episodes, baselines, embed_eps, embed_ep_infos
  ):
      """Optimize policy.
  
      Args:
          itr (int): Iteration number.
          episodes (EpisodeBatch): Batch of episodes.
          baselines (np.ndarray): Baseline predictions.
          embed_eps (np.ndarray): Embedding episodes.
          embed_ep_infos (dict): Embedding distribution information.
  
      """
      del itr
  
      policy_opt_input_values = self._policy_opt_input_values(
          episodes, baselines, embed_eps
      )
      inference_opt_input_values = self._inference_opt_input_values(
          episodes, embed_eps, embed_ep_infos
      )
  
      self._train_policy_and_encoder_networks(policy_opt_input_values)
      self._train_inference_network(inference_opt_input_values)
  
      # paths = samples_data['paths']
      fit_paths = self._evaluate(
          policy_opt_input_values, episodes, baselines, embed_ep_infos
      )
      self._visualize_distribution()
  
      logger.log("Fitting baseline...")
      self._baseline.fit(fit_paths)
  
      self._old_policy.parameters = self.policy.parameters
      self._old_policy.encoder.model.parameters = (
          self.policy.encoder.model.parameters
      )
      self._old_inference.model.parameters = (
          self._inference.model.parameters
      )
  
imports: import akro
  from dowel import Histogram, logger, tabular
  import numpy
  import scipy.stats
  import tensorflow
  from garage import InOutSpec, log_performance
  from garage.experiment import deterministic
  from garage.np import discount_cumsum, explained_variance_1d, pad_batch_array, rrse, sliding_window
  from garage.np.algos import RLAlgorithm
  from garage.tf import center_advs, compile_function, compute_advantages, concat_tensor_list, discounted_returns, flatten_inputs, graph_inputs, pad_tensor_dict, positive_advs, stack_tensor_dict_list
  from garage.tf.embeddings import StochasticEncoder
  from garage.tf.optimizers import LBFGSOptimizer
  from garage.tf.policies import TaskEmbeddingPolicy
label_desc: Use image node_id 5 for calling the TENPO obj's underlying member method code with example usage: obj._optimize_policy(itr, episodes, baselines, embed_eps, embed_ep_infos) without return types