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
code: def __init__(
      self,
      env_spec,
      policy,
      baseline,
      sampler,
      scope=None,
      discount=0.99,
      gae_lambda=1,
      center_adv=True,
      positive_adv=False,
      fixed_horizon=False,
      pg_loss="surrogate",
      lr_clip_range=0.01,
      max_kl_step=0.01,
      optimizer=None,
      optimizer_args=None,
      policy_ent_coeff=0.0,
      use_softplus_entropy=False,
      use_neg_logli_entropy=False,
      stop_entropy_gradient=False,
      entropy_method="no_entropy",
      name="NPO",
  ):
      self.policy = policy
      self._scope = scope
      self.max_episode_length = env_spec.max_episode_length
      self._env_spec = env_spec
      self._baseline = baseline
      self._discount = discount
      self._gae_lambda = gae_lambda
      self._center_adv = center_adv
      self._positive_adv = positive_adv
      self._fixed_horizon = fixed_horizon
      self._name = name
      self._name_scope = tf.name_scope(self._name)
      self._old_policy = policy.clone("old_policy")
      self._use_softplus_entropy = use_softplus_entropy
      self._use_neg_logli_entropy = use_neg_logli_entropy
      self._stop_entropy_gradient = stop_entropy_gradient
      self._pg_loss = pg_loss
      if optimizer is None:
          if optimizer_args is None:
              optimizer_args = dict()
          optimizer = FirstOrderOptimizer
  
      self._check_entropy_configuration(
          entropy_method,
          center_adv,
          stop_entropy_gradient,
          use_neg_logli_entropy,
          policy_ent_coeff,
      )
  
      if pg_loss not in ["vanilla", "surrogate", "surrogate_clip"]:
          raise ValueError("Invalid pg_loss")
  
      self._optimizer = make_optimizer(optimizer, **optimizer_args)
      self._lr_clip_range = float(lr_clip_range)
      self._max_kl_step = float(max_kl_step)
      self._policy_ent_coeff = float(policy_ent_coeff)
  
      self._f_rewards = None
      self._f_returns = None
      self._f_policy_kl = None
      self._f_policy_entropy = None
      self._policy_network = None
      self._old_policy_network = None
  
      self._episode_reward_mean = collections.deque(maxlen=100)
  
      self._sampler = sampler
  
      self._init_opt()
  
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 1 to create a new NPO object from inherited base classes: RLAlgorithm with example: obj = NPO(env_spec, policy, baseline, sampler, scope, discount, gae_lambda, center_adv, positive_adv, fixed_horizon, pg_loss, lr_clip_range, max_kl_step, optimizer, optimizer_args, policy_ent_coeff, use_softplus_entropy, use_neg_logli_entropy, stop_entropy_gradient, entropy_method, name)