Reinforcement Learning, Gaming, Deep Learning.
= env.reset() done = False ep_ret = 0.0 while not done: loc, scale = policy(state) sample = torch.normal(loc, scale) action = torch.tanh(sample) state, reward, done, info = env.step(action) ep_ret += reward.item() ep_returns.append(ep_ret) return sum(ep_returns) / episodes Create the policy class