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 __init__(
      self,
      *,
      value,
      huber_loss=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,
      )
  
      assert value in ("state", "action")
      self.value = value
  
      if huber_loss is None:
          self.huber_loss = None
      else:
          self.huber_loss = self.submodule(
              name="huber_loss",
              module=huber_loss,
              modules=parameter_modules,
              dtype="float",
              min_value=0.0,
          )
  
      self.early_reduce = early_reduce
  
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 1 to create a new Value object from inherited base classes: Objective with example: obj = Value()