Reinforcement Learning, Gaming, Deep Learning.
return torch.mean(policy_loss - self.hparams.entropy_coef * entropy) def training_epoch_end(self, training_step_outputs): if self.current_epoch % 10 == 0: average_return = test_agent(self.test_env, self.hparams.episode_length, self.policy, episodes=1) self.log("episode/Average Return",