code: def on_policy_train_batch(self, batch_info: BatchInfo):
          
          self.model.train()
  
          rollout = self.env_roller.rollout(batch_info, self.model, self.settings.number_of_steps).to_device(self.device)
  
          batch_result = self.algo.optimizer_step(
              batch_info=batch_info,
              device=self.device,
              model=self.model,
              rollout=rollout
          )
  
          batch_info['sub_batch_data'].append(batch_result)
          batch_info['frames'] = rollout.frames()
          batch_info['episode_infos'] = rollout.episode_information()
docs: Perform an 'on-policy' training step of evaluating an env and a single backpropagation step