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: Preferred move - the mean of selected moves.
docstr_tok: ["Preferred","move","-","the","mean","of","selected","moves","."]
code: def preferred_move(self):
      """Preferred move - the mean of selected moves."""
  
      def f(dist_inputs, values):
          del values
          return rl_layers.PreferredMove(
              dist_inputs, self._policy_dist.sample
          )
  
      return tl.Fn("PreferredMove", f)
  
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 10 for calling the ActorCriticJointAgent obj's underlying member method code with example usage: obj.preferred_move() and returns: tl, rl_layers