class_docstr: The Proximal Policy Optimization Algorithm aka PPO.
  
  Trains policy and value models using the PPO algortithm.
class_docstr_tok: ["The","Proximal","Policy","Optimization","Algorithm","aka","PPO",".","Trains","policy","and","value","models","using","the","PPO","algortithm","."]
docstr: PPO objective mean.
docstr_tok: ["PPO","objective","mean","."]
code: def ppo_objective_mean(self):
      """PPO objective mean."""
  
      def f(
          dist_inputs,
          values,
          returns,
          dones,
          rewards,
          actions,
          old_log_probs,
      ):
          """Clipped objective from the PPO algorithm."""
          ppo_objective = rl_layers.PPOObjective(
              dist_inputs,
              values,
              returns,
              dones,
              rewards,
              actions,
              old_log_probs,
              log_prob_fun=self._policy_dist.log_prob,
              epsilon=self._epsilon,
              normalize_advantages=self._normalize_advantages,
          )
          return jnp.mean(ppo_objective)
  
      return tl.Fn("PPOObjectiveMean", 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 11 for calling the PPOJoint obj's underlying member method code with example usage: obj.ppo_objective_mean() and returns: tl, jnp