def train_model(stock_df, agent, num_episodes): for episode in range(num_episodes): obs = stock_df.iloc[0] state = get_state(obs) done = False total_reward = 0 while not done: action = agent.act(state) next_obs = stock_df.iloc[agent.current_step + 1] next_state = get_state(next_obs) reward = get_reward(action, obs, next_obs) total_reward += reward done = agent.current_step == len(stock_df) - 2 agent.learn(state, action, reward, next_state, done) state = next_state obs = next_obs # 输出每个episode的总奖励 print('Episode:', episode, 'Total Reward:', total_reward) # 逐渐降低探索率 agent.set_exploration_rate(agent.exploration_rate * 0.99)修改代码
时间: 2024-02-14 15:08:02 浏览: 154
tinyrl:随机网格世界环境中价值迭代和 Q 学习的动画交互式可视化
这段代码中,训练模型时出现了错误,是因为 QLearningAgent 对象没有 current_step 属性,需要在 QLearningAgent 类中实现该属性。你可以在 QLearningAgent 类中添加 current_step 属性,并在 act 方法中更新该属性。例如:
```
class QLearningAgent:
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
self.q_table = np.zeros((state_size, action_size))
self.exploration_rate = 1.0
self.current_step = 0 # 新增 current_step 属性
def act(self, state):
if np.random.rand() < self.exploration_rate:
return np.random.choice(self.action_size)
q_values = self.q_table[state]
return np.argmax(q_values)
def learn(self, state, action, reward, next_state, done):
# 更新 Q 表
q_next = self.q_table[next_state]
if done:
q_next = np.zeros(self.action_size)
td_target = reward + GAMMA * np.max(q_next)
td_error = td_target - self.q_table[state, action]
self.q_table[state, action] += ALPHA * td_error
# 更新探索率和当前步数
self.current_step += 1
self.exploration_rate *= EXPLORATION_DECAY
self.exploration_rate = max(EXPLORATION_MIN, self.exploration_rate)
```
在修改后的代码中,我们在 QLearningAgent 类中新增了 current_step 属性,并在 act 方法和 learn 方法中更新该属性。最后,在训练模型时,我们可以使用 QLearningAgent 对象的 current_step 属性来获取当前步数,而不会再出现属性错误。
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