code: def end_policy_update(self):
          
          if self.time_policy_update_start:
              self.delta_policy_update = time() - self.time_policy_update_start
          else:
              self.delta_policy_update = 0
          delta_train_start = time() - self.time_training_start
          LOGGER.debug(" Policy Update Training Metrics for {}: "
                       "\n\t\tTime to update Policy: {:0.3f} s \n"
                       "\t\tTime elapsed since training: {:0.3f} s \n"
                       "\t\tTime for experience collection: {:0.3f} s \n"
                       "\t\tBuffer Length: {} \n"
                       "\t\tReturns : {:0.3f}\n"
                       .format(self.brain_name, self.delta_policy_update,
                               delta_train_start, self.delta_last_experience_collection,
                               self.last_buffer_length, self.last_mean_return))
          self._add_row(delta_train_start)
docs: Inform Metrics class that policy update has started.