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: Build input variables.
  
  Returns:
      namedtuple: Collection of variables to compute policy loss.
      namedtuple: Collection of variables to do policy optimization.
docstr_tok: ["Build","input","variables",".","Returns",":","namedtuple",":","Collection","of","variables","to","compute","policy","loss",".","namedtuple",":","Collection","of","variables","to","do","policy","optimization","."]
code: def _build_inputs(self):
      """Build input variables.
  
      Returns:
          namedtuple: Collection of variables to compute policy loss.
          namedtuple: Collection of variables to do policy optimization.
  
      """
      observation_space = self.policy.observation_space
      action_space = self.policy.action_space
  
      with tf.name_scope("inputs"):
          obs_var = observation_space.to_tf_placeholder(
              name="obs", batch_dims=2
          )
          action_var = action_space.to_tf_placeholder(
              name="action", batch_dims=2
          )
          reward_var = tf.compat.v1.placeholder(
              tf.float32, shape=[None, None], name="reward"
          )
          valid_var = tf.compat.v1.placeholder(
              tf.float32, shape=[None, None], name="valid"
          )
          baseline_var = tf.compat.v1.placeholder(
              tf.float32, shape=[None, None], name="baseline"
          )
  
          policy_state_info_vars = {
              k: tf.compat.v1.placeholder(
                  tf.float32, shape=[None] * 2 + list(shape), name=k
              )
              for k, shape in self.policy.state_info_specs
          }
          policy_state_info_vars_list = [
              policy_state_info_vars[k]
              for k in self.policy.state_info_keys
          ]
  
      augmented_obs_var = obs_var
      for k in self.policy.state_info_keys:
          extra_state_var = policy_state_info_vars[k]
          extra_state_var = tf.cast(extra_state_var, tf.float32)
          augmented_obs_var = tf.concat(
              [augmented_obs_var, extra_state_var], -1
          )
  
      self._policy_network = self.policy.build(
          augmented_obs_var, name="policy"
      )
      self._old_policy_network = self._old_policy.build(
          augmented_obs_var, name="policy"
      )
  
      policy_loss_inputs = graph_inputs(
          "PolicyLossInputs",
          action_var=action_var,
          reward_var=reward_var,
          baseline_var=baseline_var,
          valid_var=valid_var,
          policy_state_info_vars=policy_state_info_vars,
      )
      policy_opt_inputs = graph_inputs(
          "PolicyOptInputs",
          obs_var=obs_var,
          action_var=action_var,
          reward_var=reward_var,
          baseline_var=baseline_var,
          valid_var=valid_var,
          policy_state_info_vars_list=policy_state_info_vars_list,
      )
  
      return policy_loss_inputs, policy_opt_inputs
  
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 6 for calling the NPO obj's underlying member method code with example usage: obj._build_inputs() and returns: policy_loss_inputs,  policy_opt_inputs