class_docstr: Natural Policy Gradient Optimization.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      policy (garage.tf.policies.StochasticPolicy): 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.
      pg_loss (str): A string from: 'vanilla', 'surrogate',
          'surrogate_clip'. The type of loss functions to use.
      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.
      use_softplus_entropy (bool): Whether to estimate the softmax
          distribution of the entropy to prevent the entropy from being
          negative.
      use_neg_logli_entropy (bool): Whether to estimate the entropy as the
          negative log likelihood of the action.
      stop_entropy_gradient (bool): Whether to stop the entropy gradient.
      entropy_method (str): A string from: 'max', 'regularized',
          'no_entropy'. The type of entropy method to use. 'max' adds the
          dense entropy to the reward for each time step. 'regularized' adds
          the mean entropy to the surrogate objective. See
          https://arxiv.org/abs/1805.00909 for more details.
      name (str): The name of the algorithm.
  
  Note:
      sane defaults for entropy configuration:
          - entropy_method='max', center_adv=False, stop_gradient=True
            (center_adv normalizes the advantages tensor, which will
            significantly alleviate the effect of entropy. It is also
            recommended to turn off entropy gradient so that the agent
            will focus on high-entropy actions instead of increasing the
            variance of the distribution.)
          - entropy_method='regularized', stop_gradient=False,
            use_neg_logli_entropy=False
docstr: Update baselines from samples.
  
  Args:
      episodes (EpisodeBatch): Batch of episodes.
      baselines (np.ndarray): Baseline predictions.
  
  Returns:
      np.ndarray: Augment returns.
docstr_tok: ["Update","baselines","from","samples",".","Args",":","episodes","(","EpisodeBatch",")",":","Batch","of","episodes",".","baselines","(","np.ndarray",")",":","Baseline","predictions",".","Returns",":","np.ndarray",":","Augment","returns","."]
code: def _fit_baseline_with_data(self, episodes, baselines):
      """Update baselines from samples.
  
      Args:
          episodes (EpisodeBatch): Batch of episodes.
          baselines (np.ndarray): Baseline predictions.
  
      Returns:
          np.ndarray: Augment returns.
  
      """
      policy_opt_input_values = self._policy_opt_input_values(
          episodes, baselines
      )
  
      returns_tensor = self._f_returns(*policy_opt_input_values)
      returns_tensor = np.squeeze(returns_tensor, -1)
  
      paths = []
      valids = episodes.valids
      observations = episodes.padded_observations
  
      # Compute returns
      for ret, val, ob in zip(returns_tensor, valids, observations):
          returns = ret[val.astype(np.bool)]
          obs = ob[val.astype(np.bool)]
          paths.append(dict(observations=obs, returns=returns))
  
      # Fit baseline
      logger.log("Fitting baseline...")
      self._baseline.fit(paths)
      return returns_tensor
  
imports: import collections
  from dowel import logger, tabular
  import numpy
  import tensorflow
  from garage import log_performance, make_optimizer
  from garage.np import explained_variance_1d, pad_batch_array
  from garage.np.algos import RLAlgorithm
  from garage.tf import center_advs, compile_function, compute_advantages, discounted_returns, flatten_inputs, graph_inputs, positive_advs
  from garage.tf.optimizers import FirstOrderOptimizer
label_desc: Use image node_id 9 for calling the NPO obj's underlying member method code with example usage: obj._fit_baseline_with_data(episodes, baselines) and returns: returns_tensor