class_docstr: Trains a joint policy-and-value model using AWR.
class_docstr_tok: ["Trains","a","joint","policy-and-value","model","using","AWR","."]
docstr: Configures the joint AWR Trainer.
docstr_tok: ["Configures","the","joint","AWR","Trainer","."]
code: def __init__(
      self,
      task,
      value_loss_coeff=0.1,
      beta=1.0,
      w_max=20.0,
      thresholds=None,
      **kwargs,
  ):
      """Configures the joint AWR Trainer."""
      self._beta = beta
      self._w_max = w_max
      self._thresholds = thresholds
      self._value_loss_coeff = value_loss_coeff
      super().__init__(task, **kwargs)
  
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 1 to create a new AWRJoint object from inherited base classes: ActorCriticJointAgent with example: obj = AWRJoint(task, value_loss_coeff, beta, w_max, thresholds)