深度强化学习结合调度python示例
时间: 2023-09-02 17:15:53 浏览: 56
这里提供一个简单的深度强化学习结合调度的 Python 示例,使用 DQN 算法和 TensorFlow 框架实现智能体控制一个简单的游戏。
1. 导入所需库
```
import numpy as np
import tensorflow as tf
import gym
from collections import deque
import random
import time
```
2. 设置超参数和游戏环境
```
batch_size = 128
learning_rate = 0.001
gamma = 0.95
epsilon = 1.0
epsilon_min = 0.01
epsilon_decay = 0.99
memory = deque(maxlen=2000)
env = gym.make('CartPole-v0')
```
3. 定义神经网络模型
```
model = tf.keras.Sequential([
tf.keras.layers.Dense(24, input_shape=(4,), activation='relu'),
tf.keras.layers.Dense(24, activation='relu'),
tf.keras.layers.Dense(2, activation='linear')
])
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=learning_rate))
```
4. 定义动作选择函数
```
def choose_action(state):
if np.random.rand() <= epsilon:
return env.action_space.sample()
else:
q_values = model.predict(state)
return np.argmax(q_values[0])
```
5. 定义经验回放函数
```
def remember(state, action, reward, next_state, done):
memory.append((state, action, reward, next_state, done))
```
6. 定义训练函数
```
def train():
if len(memory) < batch_size:
return
minibatch = random.sample(memory, batch_size)
for state, action, reward, next_state, done in minibatch:
target = reward
if not done:
target = reward + gamma * np.amax(model.predict(next_state)[0])
q_values = model.predict(state)
q_values[0][action] = target
model.fit(state, q_values, verbose=0)
if epsilon > epsilon_min:
epsilon *= epsilon_decay
```
7. 定义主函数和调度函数
```
def main():
for episode in range(1, 1001):
state = env.reset()
state = np.reshape(state, [1, 4])
for time_step in range(1, 1001):
action = choose_action(state)
next_state, reward, done, _ = env.step(action)
next_state = np.reshape(next_state, [1, 4])
remember(state, action, reward, next_state, done)
state = next_state
if done:
print("Episode {}: Finished after {} time steps".format(episode, time_step))
break
train()
if episode % 50 == 0:
model.save("model.h5")
def scheduler():
while True:
main()
time.sleep(3600) # 每小时执行一次
```
8. 启动调度函数
```
scheduler()
```
注意:这只是一个简单的示例,实际应用中需要根据具体情况进行修改和优化。