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