Reinforcement Learning, Gaming, Deep Learning.
self.env.reset() @torch.no_grad() def __iter__(self): transitions = [] for step in range(self.episode_length): loc, scale = self.policy(self.obs) action = torch.normal(loc, scale) next_obs, reward, done, info = self.env.step(action) transitions.append((self.obs, action, reward, done)) self.obs =