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: Evaluate rewards and everything else.
  
  Args:
      policy_opt_input_values (list[np.ndarray]): Flattened
          policy optimization input values.
      episodes (EpisodeBatch): Batch of episodes.
      baselines (np.ndarray): Baseline predictions.
      embed_ep_infos (dict): Embedding distribution information.
  
  Returns:
      dict: Paths for fitting the baseline.
docstr_tok: ["Evaluate","rewards","and","everything","else",".","Args",":","policy_opt_input_values","(","list","[","np.ndarray","]",")",":","Flattened","policy","optimization","input","values",".","episodes","(","EpisodeBatch",")",":","Batch","of","episodes",".","baselines","(","np.ndarray",")",":","Baseline","predictions",".","embed_ep_infos","(","dict",")",":","Embedding","distribution","information",".","Returns",":","dict",":","Paths","for","fitting","the","baseline","."]
code: def _evaluate(
      self, policy_opt_input_values, episodes, baselines, embed_ep_infos
  ):
      """Evaluate rewards and everything else.
  
      Args:
          policy_opt_input_values (list[np.ndarray]): Flattened
              policy optimization input values.
          episodes (EpisodeBatch): Batch of episodes.
          baselines (np.ndarray): Baseline predictions.
          embed_ep_infos (dict): Embedding distribution information.
  
      Returns:
          dict: Paths for fitting the baseline.
  
      """
      # pylint: disable=too-many-statements
      fit_paths = []
      valids = episodes.valids
      observations = episodes.padded_observations
      tasks = pad_batch_array(
          episodes.env_infos["task_onehot"],
          episodes.lengths,
          self.max_episode_length,
      )
      latents = pad_batch_array(
          episodes.agent_infos["latent"],
          episodes.lengths,
          self.max_episode_length,
      )
      baselines_list = []
      for baseline, valid in zip(baselines, valids):
          baselines_list.append(baseline[valid.astype(np.bool)])
  
      # Augment reward from baselines
      rewards_tensor = self._f_rewards(*policy_opt_input_values)
      returns_tensor = self._f_returns(*policy_opt_input_values)
      returns_tensor = np.squeeze(returns_tensor, -1)
  
      env_rewards = episodes.rewards
      env_returns = [
          discount_cumsum(rwd, self._discount)
          for rwd in episodes.padded_rewards
      ]
      env_average_discounted_return = np.mean(
          [ret[0] for ret in env_returns]
      )
  
      # Recompute returns and prepare paths for fitting the baseline
      aug_rewards = []
      aug_returns = []
      for rew, ret, val, task, latent, obs in zip(
          rewards_tensor,
          returns_tensor,
          valids,
          tasks,
          latents,
          observations,
      ):
          returns = ret[val.astype(np.bool)]
          task = task[val.astype(np.bool)]
          latent = latent[val.astype(np.bool)]
          obs = obs[val.astype(np.bool)]
  
          aug_rewards.append(rew[val.astype(np.bool)])
          aug_returns.append(returns)
          fit_paths.append(
              dict(
                  observations=obs,
                  tasks=task,
                  latents=latent,
                  returns=returns,
              )
          )
      aug_rewards = concat_tensor_list(aug_rewards)
      aug_returns = concat_tensor_list(aug_returns)
  
      # Calculate effect of the entropy terms
      d_rewards = np.mean(aug_rewards - env_rewards)
      tabular.record(
          "{}/EntRewards".format(self.policy.name), d_rewards
      )
  
      aug_average_discounted_return = np.mean(
          [ret[0] for ret in returns_tensor]
      )
      d_returns = np.mean(
          aug_average_discounted_return - env_average_discounted_return
      )
      tabular.record(
          "{}/EntReturns".format(self.policy.name), d_returns
      )
  
      # Calculate explained variance
      ev = explained_variance_1d(
          np.concatenate(baselines_list), aug_returns
      )
      tabular.record(
          "{}/ExplainedVariance".format(self._baseline.name), ev
      )
  
      inference_rmse = (embed_ep_infos["mean"] - latents) ** 2.0
      inference_rmse = np.sqrt(inference_rmse.mean())
      tabular.record("Inference/RMSE", inference_rmse)
  
      inference_rrse = rrse(latents, embed_ep_infos["mean"])
      tabular.record("Inference/RRSE", inference_rrse)
  
      embed_ent = self._f_encoder_entropy(*policy_opt_input_values)
      tabular.record(
          "{}/Encoder/Entropy".format(self.policy.name), embed_ent
      )
  
      infer_ce = self._f_inference_ce(*policy_opt_input_values)
      tabular.record("Inference/CrossEntropy", infer_ce)
  
      pol_ent = self._f_policy_entropy(*policy_opt_input_values)
      pol_ent = np.sum(pol_ent) / np.sum(episodes.lengths)
      tabular.record("{}/Entropy".format(self.policy.name), pol_ent)
  
      task_ents = self._f_task_entropies(*policy_opt_input_values)
      tasks = tasks[:, 0, :]
      _, task_indices = np.nonzero(tasks)
      path_lengths = np.sum(valids, axis=1)
      for t in range(self.policy.task_space.flat_dim):
          lengths = path_lengths[task_indices == t]
          completed = lengths < self.max_episode_length
          pct_completed = np.mean(completed)
          tabular.record(
              "Tasks/EpisodeLength/t={}".format(t), np.mean(lengths)
          )
          tabular.record(
              "Tasks/TerminationRate/t={}".format(t), pct_completed
          )
          tabular.record("Tasks/Entropy/t={}".format(t), task_ents[t])
  
      return fit_paths
  
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 14 for calling the TENPO obj's underlying member method code with example usage: obj._evaluate(policy_opt_input_values, episodes, baselines, embed_ep_infos) and returns: fit_paths