运行代码next_s, reward, done, _ = env.step(a)报错not enough value to unpack(excepted 5,got 4)怎么解决
时间: 2024-05-13 08:18:17 浏览: 148
这个错误通常是由于 `step` 方法返回的元组中元素数量不足所致。请确保你的 `step` 方法返回一个长度为 5 的元组,分别是 `next_s`、`reward`、`done`、`info` 和 `_`。如果你的环境没有返回 `info`,可以将其设置为 `None`。例如:
```python
next_s, reward, done, _, _ = env.step(a)
```
如果你的环境确实没有返回 `info`,那么你可以通过这种方式忽略它。
相关问题
while not ep_done: num_steps += 1 if train_params.RENDER: self.env_wrapper.render() action = self.sess.run(self.actor_net.output, {self.state_ph:np.expand_dims(state, 0)})[0] # Add batch dimension to single state input, and remove batch dimension from single action output action += (gaussian_noise() * train_params.NOISE_DECAY**num_eps) next_state, reward, terminal = self.env_wrapper.step(action) episode_reward += reward next_state = self.env_wrapper.normalise_state(next_state) reward = self.env_wrapper.normalise_reward(reward) self.exp_buffer.append((state, action, reward)) if len(self.exp_buffer) >= train_params.N_STEP_RETURNS: state_0, action_0, reward_0 = self.exp_buffer.popleft() discounted_reward = reward_0 gamma = train_params.DISCOUNT_RATE for (_, _, r_i) in self.exp_buffer: discounted_reward += r_i * gamma gamma *= train_params.DISCOUNT_RATE run_agent_event.wait() PER_memory.add(state_0, action_0, discounted_reward, next_state, terminal, gamma) state = next_state
这段代码是主循环中的一部分,其中包含了执行动作、观察环境、更新经验缓存等操作。具体来说,算法执行以下步骤:
1. 累计步数;
2. 如果需要渲染环境,则渲染环境;
3. 使用Actor网络计算当前状态的动作;
4. 对动作添加高斯噪声;
5. 执行动作并观察新状态和奖励;
6. 累计回报;
7. 对新状态进行归一化处理;
8. 对奖励进行归一化处理;
9. 将经验添加到经验缓存中;
10. 如果经验缓存已满,则计算N步回报,并将经验添加到优先经验回放缓存中;
11. 更新状态。
在这个过程中,Actor网络用于计算当前状态下的动作,而高斯噪声则用于增加探索性,以便算法能够更好地探索环境。在执行动作之后,算法会观察新状态和奖励,并将它们添加到经验缓存中。如果经验缓存已满,算法会计算N步回报,并将经验添加到优先经验回放缓存中。最后,算法会更新状态并继续执行主循环。
import akshare as ak import numpy as np import pandas as pd import random import matplotlib.pyplot as plt class StockTradingEnv: def __init__(self): self.df = ak.stock_zh_a_daily(symbol='sh000001', adjust="qfq").iloc[::-1] self.observation_space = self.df.shape[1] self.action_space = 3 self.reset() def reset(self): self.current_step = 0 self.total_profit = 0 self.done = False self.state = self.df.iloc[self.current_step].values return self.state def step(self, action): assert self.action_space.contains(action) if action == 0: # 买入 self.buy_stock() elif action == 1: # 卖出 self.sell_stock() else: # 保持不变 pass self.current_step += 1 if self.current_step >= len(self.df) - 1: self.done = True else: self.state = self.df.iloc[self.current_step].values reward = self.get_reward() self.total_profit += reward return self.state, reward, self.done, {} def buy_stock(self): pass def sell_stock(self): pass def get_reward(self): pass class QLearningAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.995 self.learning_rate = 0.1 self.discount_factor = 0.99 self.q_table = np.zeros((self.state_size, self.action_size)) def act(self, state): if np.random.rand() <= self.epsilon: return random.randrange(self.action_size) else: return np.argmax(self.q_table[state, :]) def learn(self, state, action, reward, next_state, done): target = reward + self.discount_factor * np.max(self.q_table[next_state, :]) self.q_table[state, action] = (1 - self.learning_rate) * self.q_table[state, action] + self.learning_rate * target if self.epsilon > self.epsilon_min: self.epsilon *= self.epsilon_decay env = StockTradingEnv() agent = QLearningAgent(env.observation_space, env.action_space) for episode in range(1000): state = env.reset() done = False while not done: action = agent.act(state) next_state, reward, done, _ = env.step(action) agent.learn(state, action, reward, next_state, done) state = next_state if episode % 10 == 0: print("Episode: %d, Total Profit: %f" % (episode, env.total_profit)) agent.save_model("model-%d.h5" % episode) def plot_profit(env, title): plt.figure(figsize=(12, 6)) plt.plot(env.df.index, env.df.close, label="Price") plt.plot(env.df.index, env.profits, label="Profits") plt.legend() plt.title(title) plt.show() env = StockTradingEnv() agent = QLearningAgent(env.observation_space, env.action_space) agent.load_model("model-100.h5") state = env.reset() done = False while not done: action = agent.act(state) next_state, reward, done, _ = env.step(action) state = next_state plot_profit(env, "QLearning Trading Strategy")优化代码
1. 对于环境类 `StockTradingEnv`,可以考虑将 `buy_stock` 和 `sell_stock` 方法的具体实现写入 `step` 方法中,避免方法数量过多。
2. 可以将 `get_reward` 方法中的具体实现改为直接计算当前持仓的收益。
3. 在循环训练过程中,可以记录每个 episode 的总收益,并将这些数据保存下来,在训练完成后进行可视化分析。
4. 可以添加更多的参数来控制训练过程,比如学习率、衰减系数等。
5. 可以将 QLearningAgent 类中的方法进行整理和封装,提高代码的可读性和可维护性。同时,也可以添加一些对模型进行保存和加载的方法,便于模型的重用和共享。
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