class_docstr: Deterministic policy gradient objective (specification key: `det_policy_gradient`).
  
  Args:
      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 loss(
      self,
      *,
      states,
      horizons,
      internals,
      auxiliaries,
      actions,
      reward,
      reference,
      policy,
      baseline,
  ):
      # actions = self.reference(
      #     states=states, horizons=horizons, internals=internals, auxiliaries=auxiliaries,
      #     actions=actions, policy=policy
      # )
  
      deterministic = tf_util.constant(value=True, dtype="bool")
      actions, _ = policy.act(
          states=states,
          horizons=horizons,
          internals=internals,
          auxiliaries=auxiliaries,
          deterministic=deterministic,
          independent=True,
      )
  
      action_value = baseline.action_value(
          states=states,
          horizons=horizons,
          internals=internals,
          auxiliaries=auxiliaries,
          actions=actions,
      )
  
      return -action_value
  
file_path: tensorforce/tensorforce/core/objectives/deterministic_policy_gradient.py
imports: import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import TensorSpec, tf_function, tf_util
  from tensorforce.core.objectives import Objective
label_desc: Use image node_id 6 for calling the DeterministicPolicyGradient obj's underlying member method code with example usage: obj.loss() and returns: unknown