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: Trains RL for one epoch.
docstr_tok: ["Trains","RL","for","one","epoch","."]
code: def train_epoch(self):
      """Trains RL for one epoch."""
      n_evals = rl_training.remaining_evals(
          self._trainer.step,
          self._epoch,
          self._train_steps_per_epoch,
          self._supervised_evals_per_epoch,
      )
      for _ in range(n_evals):
          self._trainer.train_epoch(
              self._train_steps_per_epoch
              // self._supervised_evals_per_epoch,
              self._supervised_eval_steps,
          )
  
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 12 for calling the ActorCriticJointAgent obj's underlying member method code with example usage: obj.train_epoch() without return types