PyTorch：强化学习+OpenAI不是学习

我正在尝试建立一个模型，该模型将使用演员批评策略的强化学习来预测股票的买入或卖出信号。 一般来说，我对机器学习和Pytork都是新手，在我对这个问题的研究中，我意识到我没有以某种方式学习任何东西。。。我的意思是，如果我只是在观察wandb图是如何演变的，那么看起来我在学习一些东西

此外，我通过以下两个功能保存和加载模型：

    def save_model(self, path: str, name: str):
        torch.save(self.actor.state_dict(), os.path.join(path, f"{name}_actor"))
        torch.save(self.critic.state_dict(), os.path.join(path, f"{name}_critic"))

    def load_model(self, path: str, name: str):
        self.actor.load_state_dict(torch.load(os.path.join(path, f"{name}_actor")))
        self.critic.load_state_dict(torch.load(os.path.join(path, f"{name}_critic")))

但我发现真正奇怪的是，在我的选择动作功能中，我总是选择动作1，在2个买入或卖出中卖出。动作与1不同的唯一时间是当y的随机值大于ε时

    def select_action(self, state, epsilon):
        random_for_egreedy = torch.rand(1)[0]
        if random_for_egreedy > epsilon:
            with torch.no_grad():
                state = torch.Tensor(state.values).to(device)
                actor_action = self.actor(state)
                action = torch.argmax(actor_action)
                action = action.item()
        else:
            action = self.gym.action_space.sample()
        return action

这是我的优化功能：

    def optimize(self):
        if len(self.memory) < self.config.batch_size:
            return
        self.optimizer_actor.zero_grad()
        self.optimizer_critic.zero_grad()

        state, action, new_state, reward, done = self.memory.sample(batch_size=self.config.batch_size)

        state = torch.Tensor(np.array(state)).to(device)
        new_state = torch.Tensor(np.array(new_state)).to(device)
        reward = torch.Tensor(reward).to(device)
        action = torch.LongTensor(action).to(device)
        done = torch.Tensor(done).to(device)
        dist = torch.distributions.Categorical(self.actor(state))
        advantage = reward + (1 - done) * self.config.gamma * self.critic(new_state) - self.critic(state)

        critic_loss = advantage.pow(2).mean()
        self.optimizer_critic.zero_grad()
        critic_loss.backward()
        self.optimizer_critic.step()

        actor_loss = -dist.log_prob(action) * advantage.detach()
        self.optimizer_actor.zero_grad()
        actor_loss.mean().backward()
        self.optimizer_actor.step()

        wandb.log({"Actor Loss": actor_loss.mean(), "Critic Loss": critic_loss})

你们谁能告诉我我错过了什么吗？还是我做错了什么

for ep in range(conf.num_episode):
    state = env.reset()
    step = 0
    # qnet_agent.reset_running_loss()

    wandb.log({"Episode": ep})
    if ep % save_after_episode == 0:
        qnet_agent.save_model("checkpoints", model_save_name)

    while True:
        wandb.log({"step": step})
        step += 1
        frames_total += 1

        epsilon = calculate_epsilon(frames_total)

        action = qnet_agent.select_action(state, epsilon)

        wandb.log({"last action": action})

        new_state, reward, done, info = env.step(action)
        wandb.log({"Current profit": info['current_profit']})

        wandb.log({"Total profit": info['total_profit']})
        wandb.log({"reward": reward})

        memory.push(state, action, new_state, reward, done)
        qnet_agent.optimize()
        state = new_state

        if done:
            steps_total.append(step)
            break