Reinforcement Learning, Gaming, Deep Learning.
plt.yticks(ticks=[100, 50, 0], labels=['-0.418', '0', '0.418']) @torch.no_grad() def create_video(env, episode_length, policy=None): qp_array = [] state = env.reset() for i in range(episode_length): if policy: loc, scale = policy(state) sample = torch.normal(loc, scale) action = torch.tanh(sample)