class_docstr: Policy which parametrizes an action-value function, conditioned on the output of a neural
  network processing the input state (specification key: `parametrized_action_value`).
  
  Args:
      network ('auto' | specification): Policy network configuration, see
          [networks](../modules/networks.html)
          (<span style="color:#00C000"><b>default</b></span>: 'auto', automatically configured
          network).
      device (string): Device name
          (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
      l2_regularization (float >= 0.0): Scalar controlling L2 regularization
          (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
      name (string): <span style="color:#0000C0"><b>internal use</b></span>.
      states_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      auxiliaries_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      internals_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      actions_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
code: def __init__(
      self,
      network="auto",
      *,
      device=None,
      l2_regularization=None,
      name=None,
      states_spec=None,
      auxiliaries_spec=None,
      internals_spec=None,
      actions_spec=None,
  ):
      super().__init__(
          device=device,
          l2_regularization=l2_regularization,
          name=name,
          states_spec=states_spec,
          auxiliaries_spec=auxiliaries_spec,
          actions_spec=actions_spec,
      )
  
      inputs_spec = TensorsSpec()
      if self.states_spec.is_singleton():
          inputs_spec["states"] = self.states_spec.singleton()
      else:
          inputs_spec["states"] = self.states_spec
      if self.actions_spec.is_singleton():
          inputs_spec["actions"] = self.actions_spec.singleton()
      else:
          inputs_spec["actions"] = self.actions_spec
      ParametrizedPolicy.__init__(
          self=self, network=network, inputs_spec=inputs_spec
      )
      output_spec = self.network.output_spec()
  
      # Action value
      self.value = self.submodule(
          name="value",
          module="linear",
          modules=layer_modules,
          size=0,
          input_spec=output_spec,
      )
  
file_path: tensorforce/tensorforce/core/policies/parametrized_action_value.py
imports: from tensorforce.core import layer_modules, TensorDict, TensorsSpec, tf_function, tf_util
  from tensorforce.core.policies import ActionValue, ParametrizedPolicy
label_desc: Use image node_id 1 to create a new ParametrizedActionValue object from inherited base classes: ActionValue, ParametrizedPolicy with example: obj = ParametrizedActionValue(network)