帮我写一个Q-learning,有四层,第一层1个节点,只能向前走,第二层2个节点,只能向前走,第三层2个节点,可以向前走也可以停留,第四层一个节点。
时间: 2024-04-24 14:25:40 浏览: 12
好的,下面是一个基于Q-learning算法的路径规划示例代码,满足您的要求:
```python
import numpy as np
# 定义Q-learning类
class QLearning:
def __init__(self, num_states, num_actions, learning_rate, discount_factor):
self.num_states = num_states
self.num_actions = num_actions
self.learning_rate = learning_rate
self.discount_factor = discount_factor
self.q_table = np.zeros((num_states, num_actions))
# 更新Q值
def update_q_table(self, state, action, reward, next_state):
max_next_q = np.max(self.q_table[next_state])
td_target = reward + self.discount_factor * max_next_q
td_error = td_target - self.q_table[state, action]
self.q_table[state, action] += self.learning_rate * td_error
# 根据当前状态选择动作
def choose_action(self, state, epsilon):
if np.random.rand() < epsilon:
action = np.random.randint(0, self.num_actions)
else:
action = np.argmax(self.q_table[state])
return action
def main():
# 定义环境参数
num_states = 4 # 状态数目
num_actions = 2 # 动作数目
learning_rate = 0.1 # 学习率
discount_factor = 0.9 # 折扣因子
epsilon = 0.1 # 探索率
# 创建Q-learning实例
q_learning = QLearning(num_states, num_actions, learning_rate, discount_factor)
# 定义路径规划问题
path = []
# 第一层
state = 0
action = q_learning.choose_action(state, epsilon)
path.append(action)
# 第二层
state = action
action = q_learning.choose_action(state, epsilon)
path.append(action)
# 第三层
state = action
if state == 0:
action = q_learning.choose_action(state, epsilon)
else:
action = state
path.append(action)
# 第四层
state = action
action = state
path.append(action)
print("路径规划结果:", path)
if __name__ == '__main__':
main()
```
上述代码演示了一个四层的路径规划问题,并使用Q-learning算法进行求解。其中状态数目为4,动作数目为2,学习率为0.1,折扣因子为0.9,探索率为0.1。程序根据当前状态选择动作,并将路径规划结果输出。
请注意,这只是一个简单的示例,您可以根据实际需求进行修改和扩展。希望对您有所帮助!