class_docstr: The A2C algorithm.
  
  Trains policy and value models using the A2C algortithm.
class_docstr_tok: ["The","A2C","algorithm",".","Trains","policy","and","value","models","using","the","A2C","algortithm","."]
docstr: Entropy layer.
docstr_tok: ["Entropy","layer","."]
code: def entropy_loss(self):
      """Entropy layer."""
  
      def f(dist_inputs, values, returns, dones, rewards, actions):
          del values, returns, dones, rewards, actions
          return rl_layers.EntropyLoss(
              dist_inputs,
              distribution=self._policy_dist,
              coeff=self._entropy_coeff,
          )
  
      return tl.Fn("EntropyLoss", 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 4 for calling the A2CJoint obj's underlying member method code with example usage: obj.entropy_loss() and returns: tl, rl_layers