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 get_architecture(self):
      return "Network:  {}\nAction-value:  {}".format(
          self.network.get_architecture().replace("\n", "\n    "),
          self.value.get_architecture().replace("\n", "\n    "),
      )
  
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 2 for calling the ParametrizedActionValue obj's underlying member method code with example usage: obj.get_architecture() and returns: str