下面是一个使用Python编写的连续Mountain Car问题的解题代码示例：
```python
import gym
import numpy as np
# 定义连续Mountain Car问题的解决类
class ContinuousMountainCarSolver:
    def __init__(self, env):
        v = env
        self.num_states = env.observation_space.shape[0]
        self.num_actions = env.action_space.shape[0]
        self.max_iterations = 10000
        self.learning_rate = 0.01
        self.gamma = 0.99
        self.epsilon = 1.0
        self.epsilon_decay = 0.995
        self.epsilon_min = 0.01
        self.weights = np.random.rand(self.num_states)
    # 使用当前策略选择动作
    def choose_action(self, state):
        if np.random.rand() < self.epsilon:
            v.action_space.sample()
        return np.argmax(np.dot(state, self.weights))
    # 更新权重
    def update_weights(self, state, action, reward, next_state):
        target = reward + self.gamma * np.max(np.dot(next_state, self.weights))
        error = target - np.dot(state, self.weights)
        self.weights += self.learning_rate * error * state
    # 解决连续Mountain Car问题
    def solve(self):
        for episode in range(self.max_iterations):
            state = set()
            state = np.reshape(state, [1, self.num_states])
            done = False
            timesteps = 0
            while not done:
                timesteps += 1
                der()
                action = self.choose_action(state)
                next_state, reward, done, _ = v.step(action)
                next_state = np.reshape(next_state, [1, self.num_states])
                self.update_weights(state, action, reward, next_state)
python代码转换                state = next_state
            if done:
                print(f"Episode {episode} completed in {timesteps} timesteps.")
                if episode > 100:
                    self.epsilon *= self.epsilon_decay
                    self.epsilon = max(self.epsilon_min, self.epsilon)
# 创建连续Mountain Car环境
env = gym.make('MountainCarContinuous-v0')
# 创建连续Mountain Car问题的解决实例
solver = ContinuousMountainCarSolver(env)
# 解决连续Mountain Car问题
solver.solve()
# 关闭环境
env.close()
```
这段代码使用了OpenAI Gym库中的`gym.make()`函数来创建连续Mountain Car环境。然后，定义了一个`ContinuousMountainCarSolver`类用于解决该问题。在`solve()`方法中，使用ε-greedy策略选择动作，更新权重，并在每个episode结束时进行一些必要的处理。最后，通过调用`solve()`方法来解决连续Mountain Car问题。
请确保已正确安装`gym`库，并且已经安装了支持连续动作空间的`MountainCarContinuous-v0`环境。

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