class_docstr: Policy gradient objective, which maximizes the log-likelihood or likelihood-ratio scaled by the
  target reward value (specification key: `policy_gradient`).
  
  Args:
      importance_sampling (bool): Whether to use the importance sampling version of the policy
          gradient objective
          (<span style="color:#00C000"><b>default</b></span>: false).
      clipping_value (parameter, float > 0.0): Clipping threshold for the maximized value
          (<span style="color:#00C000"><b>default</b></span>: no clipping).
      early_reduce (bool): Whether to compute objective for aggregated likelihood instead of
          likelihood per action (<span style="color:#00C000"><b>default</b></span>: true).
      name (string): <span style="color:#0000C0"><b>internal use</b></span>.
      states_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      internals_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      auxiliaries_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      actions_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      reward_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
code: def __init__(
      self,
      *,
      importance_sampling=False,
      clipping_value=None,
      early_reduce=True,
      name=None,
      states_spec=None,
      internals_spec=None,
      auxiliaries_spec=None,
      actions_spec=None,
      reward_spec=None,
  ):
      super().__init__(
          name=name,
          states_spec=states_spec,
          internals_spec=internals_spec,
          auxiliaries_spec=auxiliaries_spec,
          actions_spec=actions_spec,
          reward_spec=reward_spec,
      )
  
      self.importance_sampling = importance_sampling
  
      if clipping_value is None:
          self.clipping_value = None
      else:
          self.clipping_value = self.submodule(
              name="clipping_value",
              module=clipping_value,
              modules=parameter_modules,
              dtype="float",
              min_value=0.0,
          )
  
      self.early_reduce = early_reduce
  
file_path: tensorforce/tensorforce/core/objectives/policy_gradient.py
imports: import numpy
  import tensorflow
  from tensorforce import util
  from tensorforce.core import parameter_modules, TensorSpec, tf_function, tf_util
  from tensorforce.core.objectives import Objective
label_desc: Use image node_id 1 to create a new PolicyGradient object from inherited base classes: Objective with example: obj = PolicyGradient()