class_docstr: Value approximation objective, which minimizes the L2-distance between the state-(action-)value
  estimate and the target reward value
  (specification key: `value`, `state_value`, `action_value`).
  
  Args:
      value ("state" | "action"): Whether to approximate the state- or state-action-value
          (<span style="color:#C00000"><b>required</b></span>).
      huber_loss (parameter, float > 0.0): Huber loss threshold
          (<span style="color:#00C000"><b>default</b></span>: no huber loss).
      early_reduce (bool): Whether to compute objective for aggregated value instead of value 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 required_policy_fns(self):
      if self.value == "state":
          return ("state_value",)
      elif self.value == "action":
          return ("action_value",)
  
file_path: tensorforce/tensorforce/core/objectives/value.py
imports: import tensorflow
  from tensorforce.core import parameter_modules, TensorSpec, tf_function, tf_util
  from tensorforce.core.objectives import Objective
label_desc: Use image node_id 2 for calling the Value obj's underlying member method code with example usage: obj.required_policy_fns() and returns: str, str