Reinforcement Learning, Gaming, Deep Learning.
* self.gamma * running_return return_b[row] = running_return num_samples = self.env.num_envs * self.episode_length obs_b = obs_b.view(num_samples, -1) action_b = action_b.view(num_samples, -1) return_b = return_b.view(num_samples, -1) idx = list(range(num_samples)) random.shuffle(idx) for i in idx: