class_docstr: Trains a joint policy-and-value model using actor-critic methods.
class_docstr_tok: ["Trains","a","joint","policy-and-value","model","using","actor-critic","methods","."]
docstr: Joint policy and value loss layer.
docstr_tok: ["Joint","policy","and","value","loss","layer","."]
code: def joint_loss(self):
      """Joint policy and value loss layer."""
      return NotImplementedError
  
imports: import functools
  from trax import data
  from trax import layers
  from trax import supervised
  from trax.fastmath import numpy
  from trax.fastmath import stop_gradient
  from trax.rl import actor_critic
  from trax.rl import distributions
  from trax.rl import rl_layers
  from trax.rl import training
  from trax.supervised import lr_schedules
label_desc: Use image node_id 4 for calling the ActorCriticJointAgent obj's underlying member method code with example usage: obj.joint_loss() and returns: NotImplementedError