用python编写基于强化学习的pid控制倒立摆的代码

好的，下面是基于强化学习的PID控制倒立摆的Python代码。这个代码使用了深度强化学习库TensorFlow和OpenAI Gym环境。

首先，我们需要安装依赖库：

!pip install tensorflow gym

接下来，我们编写代码：

import gym
import numpy as np
import tensorflow as tf

# 定义PID控制器
class PIDController:
    def __init__(self, P=0.1, I=0.0, D=0.0):
        self.Kp = P
        self.Ki = I
        self.Kd = D
        self.last_error = 0.0
        self.integral_error = 0.0

    def control(self, error, dt):
        self.integral_error += error * dt
        derivative_error = (error - self.last_error) / dt
        output = self.Kp * error + self.Ki * self.integral_error + self.Kd * derivative_error
        self.last_error = error
        return output

# 定义神经网络模型
class NeuralNetwork:
    def __init__(self, input_size, output_size, hidden_size=64, learning_rate=0.001):
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_size = hidden_size
        self.learning_rate = learning_rate

        self.x = tf.placeholder(tf.float32, shape=[None, self.input_size])
        self.y = tf.placeholder(tf.float32, shape=[None, self.output_size])

        self.W1 = tf.Variable(tf.random_normal([self.input_size, self.hidden_size]))
        self.b1 = tf.Variable(tf.random_normal([self.hidden_size]))
        self.W2 = tf.Variable(tf.random_normal([self.hidden_size, self.output_size]))
        self.b2 = tf.Variable(tf.random_normal([self.output_size]))

        self.hidden_layer = tf.nn.relu(tf.add(tf.matmul(self.x, self.W1), self.b1))
        self.output_layer = tf.add(tf.matmul(self.hidden_layer, self.W2), self.b2)

        self.loss = tf.reduce_mean(tf.square(self.y - self.output_layer))
        self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.loss)

        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())

    def train(self, inputs, targets):
        _, loss = self.sess.run([self.optimizer, self.loss], feed_dict={self.x: inputs, self.y: targets})
        return loss

    def predict(self, inputs):
        return self.sess.run(self.output_layer, feed_dict={self.x: inputs})

# 定义环境和参数
env = gym.make('InvertedPendulum-v2')
state_size = env.observation_space.shape[0]
action_size = env.action_space.shape[0]

PID = PIDController(P=5.0, I=0.0, D=0.5)
NN = NeuralNetwork(state_size, action_size)

max_episodes = 1000
max_steps = 1000
gamma = 0.99
epsilon = 1.0
epsilon_min = 0.01
epsilon_decay = 0.995

# 训练模型
for episode in range(max_episodes):
    state = env.reset()
    total_reward = 0
    for step in range(max_steps):
        if np.random.random() < epsilon:
            action = env.action_space.sample()
        else:
            action = NN.predict([state])[0]

        next_state, reward, done, _ = env.step(action)
        error = next_state[2]  # 使用摆杆的角速度作为误差信号
        control_signal = PID.control(error, env.dt)

        target = action + gamma * control_signal
        target = np.clip(target, -1.0, 1.0)
        target = np.expand_dims(target, axis=0)

        loss = NN.train(np.array([state]), target)
        state = next_state
        total_reward += reward

        if done:
            break

    print("Episode: {} Total Reward: {:.2f} Epsilon: {:.2f} Loss: {:.4f}".format(
        episode + 1, total_reward, epsilon, loss))

    epsilon = max(epsilon_min, epsilon_decay * epsilon)

代码的思路是：在每个时间步中，使用神经网络预测下一个动作，并使用PID控制器根据摆杆的角速度计算控制信号。然后将控制信号作为目标值，与神经网络预测的动作值计算损失值，并使用反向传播算法训练神经网络模型。

代码中使用了OpenAI Gym中的倒立摆环境，可以使用以下代码进行安装：

!pip install gym

然后，可以使用以下代码运行倒立摆环境：

import gym

env = gym.make('InvertedPendulum-v2')
env.reset()
for _ in range(1000):
    env.render()
    env.step(env.action_space.sample())
env.close()

以上就是基于强化学习的PID控制倒立摆的Python代码。