我在我的AI中使用了带有LSTM的神经网络Actor Critic TD3模型。对于每次训练，我都会创建一批顺序数据并训练我的AI。
请问有专家能否帮助我了解一下，这种AI是否也需要使用epoch？并且一般来说，使用这段代码可以运行多少个epoch，因为我在一个训练步骤中创建了许多批次，这样做是否也可行使用epoch？
以下是训练步骤代码
def train( self, replay_buffer, iterations, batch_size=50, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2, ): b_state = torch.Tensor([]) b_next_state = torch.Tensor([]) b_done = torch.Tensor([]) b_reward = torch.Tensor([]) b_action = torch.Tensor([]) for it in range(iterations): # print ('it: ', it, ' iterations: ', iterations) # Step 4: We sample a batch of transitions (s, s’, a, r) from the memory (batch_states, batch_next_states, batch_actions, batch_rewards, batch_dones) = \ replay_buffer.sample(batch_size) batch_states = batch_states.astype(float) batch_next_states = batch_next_states.astype(float) batch_actions = batch_actions.astype(float) batch_rewards = batch_rewards.astype(float) batch_dones = batch_dones.astype(float) state = torch.from_numpy(batch_states) next_state = torch.from_numpy(batch_next_states) action = torch.from_numpy(batch_actions) reward = torch.from_numpy(batch_rewards) done = torch.from_numpy(batch_dones) b_size = 1 seq_len = state.shape[0] batch = b_size input_size = state_dim state = torch.reshape(state, ( 1,seq_len, state_dim)) next_state = torch.reshape(next_state, ( 1,seq_len, state_dim)) done = torch.reshape(done, ( 1,seq_len, 1)) reward = torch.reshape(reward, ( 1, seq_len, 1)) action = torch.reshape(action, ( 1, seq_len, action_dim)) b_state = torch.cat((b_state, state),dim=0) b_next_state = torch.cat((b_next_state, next_state),dim=0) b_done = torch.cat((b_done, done),dim=0) b_reward = torch.cat((b_reward, reward),dim=0) b_action = torch.cat((b_action, action),dim=0) # state = torch.reshape(state, (seq_len, 1, state_dim)) # next_state = torch.reshape(next_state, (seq_len, 1, # state_dim)) # done = torch.reshape(done, (seq_len, 1, 1)) # reward = torch.reshape(reward, (seq_len, 1, 1)) # action = torch.reshape(action, (seq_len, 1, action_dim)) # b_state = torch.cat((b_state, state),dim=1) # b_next_state = torch.cat((b_next_state, next_state),dim=1) # b_done = torch.cat((b_done, done),dim=1) # b_reward = torch.cat((b_reward, reward),dim=1) # b_action = torch.cat((b_action, action),dim=1) print("dim state:",b_state.shape) # for h and c shape (num_layers * num_directions, batch, hidden_size) ha0 = torch.zeros(lstm_layers, b_state.shape[0], state_dim) ca0 = torch.zeros(lstm_layers, b_state.shape[0], state_dim) hc0 = torch.zeros(lstm_layers, b_state.shape[0], state_dim + action_dim) cc0 = torch.zeros(lstm_layers, b_state.shape[0], state_dim + action_dim) # Step 5: From the next state s’, the Actor target plays the next action a’ b_next_action = self.actor_target(b_next_state, (ha0, ca0)) b_next_action = b_next_action[0] # Step 6: We add Gaussian noise to this next action a’ and we clamp it in a range of values supported by the environment noise = torch.Tensor(b_next_action).data.normal_(0, policy_noise) noise = noise.clamp(-noise_clip, noise_clip) b_next_action = (b_next_action + noise).clamp(-self.max_action, self.max_action) # Step 7: The two Critic targets take each the couple (s’, a’) as input and return two Q-values Qt1(s’,a’) and Qt2(s’,a’) as outputs result = self.critic_target(b_next_state, b_next_action, (hc0,cc0)) target_Q1 = result[0] target_Q2 = result[1] # Step 8: We keep the minimum of these two Q-values: min(Qt1, Qt2) target_Q = torch.min(target_Q1, target_Q2).double() # Step 9: We get the final target of the two Critic models, which is: Qt = r + γ * min(Qt1, Qt2), where γ is the discount factor target_Q = b_reward + (1 - b_done) * discount * target_Q # Step 10: The two Critic models take each the couple (s, a) as input and return two Q-values Q1(s,a) and Q2(s,a) as outputs b_action_reshape = torch.reshape(b_action, b_next_action.shape) result = self.critic(b_state, b_action_reshape, (hc0, cc0)) current_Q1 = result[0] current_Q2 = result[1] # Step 11: We compute the loss coming from the two Critic models: Critic Loss = MSE_Loss(Q1(s,a), Qt) + MSE_Loss(Q2(s,a), Qt) critic_loss = F.mse_loss(current_Q1, target_Q) \ + F.mse_loss(current_Q2, target_Q) # Step 12: We backpropagate this Critic loss and update the parameters of the two Critic models with a SGD optimizer self.critic_optimizer.zero_grad() critic_loss.backward() self.critic_optimizer.step() # Step 13: Once every two iterations, we update our Actor model by performing gradient ascent on the output of the first Critic model out = self.actor(b_state, (ha0, ca0)) out = out[0] (actor_loss, hx, cx) = self.critic.Q1(b_state, out, (hc0,cc0)) actor_loss = -1 * actor_loss.mean() self.actor_optimizer.zero_grad() actor_loss.backward() self.actor_optimizer.step() # Step 14: Still once every two iterations, we update the weights of the Actor target by polyak averaging for (param, target_param) in zip(self.actor.parameters(), self.actor_target.parameters()): target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data) # Step 15: Still once every two iterations, we update the weights of the Critic target by polyak averaging for (param, target_param) in zip(self.critic.parameters(), self.critic_target.parameters()): target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)回答：
首先我要说，通常强化学习需要大量的训练。然而，这在很大程度上取决于你试图解决的问题的复杂性。如果你的模型很复杂（例如使用LSTM），情况可能会更糟。在训练一个玩Atari游戏的智能体时，你可能需要多达100万个回合（这取决于游戏和所使用的方法）。
关于epoch，如果你指的是重复使用某个特定回合（或一组回合）进行训练，那么这将取决于你是使用在线策略还是离线策略（对于离线策略，这类似于经验回放，使用epoch这个词是不正确的）。Actor-Critic方法通常是基于在线策略的，这意味着它们在每个训练阶段都需要新数据。一旦一个回合被用于训练，就不应该再次使用它。关于在线策略和离线策略的区别，我建议你查看Sutton的书以获取更多信息。
