用python编写基于强化学习的pid调参算法的代码

下面是一个基于强化学习的PID调参算法的Python代码示例，使用了Deep Q-Learning算法：

import gym
from gym import spaces
import numpy as np

class PIDEnv(gym.Env):
    metadata = {'render.modes': ['human']}

    def __init__(self):
        self.action_space = spaces.Box(low=np.array([-1]), high=np.array([1]), dtype=np.float32)
        self.observation_space = spaces.Box(low=np.array([0, 0, 0]), high=np.array([100, 100, 100]), dtype=np.float32)
        self.target = 50
        self.current = 0
        self.timestep = 0.01
        self.max_timestep = 1000
        self.state = np.array([self.current, 0, 0])
        self.pid_params = [0, 0, 0]

    def step(self, action):
        self.current += action[0]
        error = self.target - self.current
        self.pid_params[0] += self.timestep * error
        self.pid_params[1] = error / self.timestep
        self.pid_params[2] = (error - self.state[1]) / self.timestep
        reward = -abs(error)
        self.state = np.array([self.current, error, self.pid_params[0]])
        self.timestep += 1
        done = self.timestep >= self.max_timestep
        return self.state, reward, done, {}

    def reset(self):
        self.current = 0
        self.timestep = 0.01
        self.pid_params = [0, 0, 0]
        self.state = np.array([self.current, 0, 0])
        return self.state

    def render(self, mode='human'):
        print(f"Current: {self.current}, Error: {self.state[1]}, Integral: {self.pid_params[0]}")

    def close(self):
        pass


class Agent:
    def __init__(self, env):
        self.env = env
        self.memory = []
        self.gamma = 0.99
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.batch_size = 32
        self.learning_rate = 0.001
        self.model = self.create_model()

    def create_model(self):
        model = Sequential()
        state_shape = self.env.observation_space.shape
        model.add(Dense(24, input_dim=state_shape[0], activation="relu"))
        model.add(Dense(24, activation="relu"))
        model.add(Dense(self.env.action_space.shape[0]))
        model.compile(loss="mean_squared_error", optimizer=Adam(lr=self.learning_rate))
        return model

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return self.env.action_space.sample()
        return self.model.predict(state)[0]

    def replay(self):
        if len(self.memory) < self.batch_size:
            return
        samples = np.random.sample(self.memory, self.batch_size)
        for state, action, reward, next_state, done in samples:
            target = reward
            if not done:
                target = reward + self.gamma * np.amax(self.model.predict(next_state)[0])
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

    def load(self, name):
        self.model.load_weights(name)

    def save(self, name):
        self.model.save_weights(name)


if __name__ == '__main__':
    env = PIDEnv()
    agent = Agent(env)
    num_episodes = 1000
    for e in range(num_episodes):
        state = env.reset()
        state = np.reshape(state, [1, env.observation_space.shape[0]])
        for time in range(500):
            action = agent.act(state)
            next_state, reward, done, _ = env.step(action)
            next_state = np.reshape(next_state, [1, env.observation_space.shape[0]])
            agent.remember(state, action, reward, next_state, done)
            state = next_state
            agent.replay()
            if done:
                print(f"episode: {e}/{num_episodes}, score: {time}, e: {agent.epsilon}")
                break

在这个示例中，我们定义了一个PIDEnv类，它是一个OpenAI Gym环境，用于模拟一个带有PID控制器的系统。我们还定义了一个Agent类，它使用Deep Q-Learning算法来选择动作，并使用经验回放来训练模型。在主函数中，我们运行了1000个回合，并在每个回合中训练了代理。