用python写一段ddpg案例

当然，我可以为你提供一个基于Python的DDPG（Deep Deterministic Policy Gradient）算法的简单案例。

首先，我们需要导入一些必要的库，如TensorFlow和NumPy：

import tensorflow as tf
import numpy as np

然后，我们定义DDPG的Actor网络和Critic网络：

class ActorNetwork:
    def __init__(self, state_dim, action_dim, action_bound, learning_rate, tau):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.action_bound = action_bound
        self.learning_rate = learning_rate
        self.tau = tau

        self.inputs, self.outputs, self.scaled_outputs = self.build_network()

        self.network_params = tf.trainable_variables()

    def build_network(self):
        inputs = tf.placeholder(tf.float32, [None, self.state_dim])
        W1 = tf.Variable(tf.random_uniform([self.state_dim, 400], -1e-3, 1e-3))
        b1 = tf.Variable(tf.random_uniform([400], -1e-3, 1e-3))
        h1 = tf.nn.relu(tf.matmul(inputs, W1) + b1)

        W2 = tf.Variable(tf.random_uniform([400, 300], -1e-3, 1e-3))
        b2 = tf.Variable(tf.random_uniform([300], -1e-3, 1e-3))
        h2 = tf.nn.relu(tf.matmul(h1, W2) + b2)

        W3 = tf.Variable(tf.random_uniform([300, self.action_dim], -3e-3, 3e-3))
        b3 = tf.Variable(tf.random_uniform([self.action_dim], -3e-3, 3e-3))
        outputs = tf.nn.tanh(tf.matmul(h2, W3) + b3)

        scaled_outputs = tf.multiply(outputs, self.action_bound)

        return inputs, outputs, scaled_outputs

class CriticNetwork:
    def __init__(self, state_dim, action_dim, learning_rate, tau):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.learning_rate = learning_rate
        self.tau = tau

        self.inputs, self.action, self.outputs = self.build_network()

        self.network_params = tf.trainable_variables()

    def build_network(self):
        inputs = tf.placeholder(tf.float32, [None, self.state_dim])
        action = tf.placeholder(tf.float32, [None, self.action_dim])

        W1 = tf.Variable(tf.random_uniform([self.state_dim, 400], -1e-3, 1e-3))
        b1 = tf.Variable(tf.random_uniform([400], -1e-3, 1e-3))
        h1 = tf.nn.relu(tf.matmul(inputs, W1) + b1)

        h1_action = tf.concat([h1, action], 1)

        W2 = tf.Variable(tf.random_uniform([400 + self.action_dim, 300], -1e-3, 1e-3))
        b2 = tf.Variable(tf.random_uniform([300], -1e-3, 1e-3))
        h2 = tf.nn.relu(tf.matmul(h1_action, W2) + b2)

        W3 = tf.Variable(tf.random_uniform([300, 1], -3e-3, 3e-3))
        b3 = tf.Variable(tf.random_uniform([1], -3e-3, 3e-3))
        outputs = tf.matmul(h2, W3) + b3

        return inputs, action, outputs

接下来，我们定义DDPG的经验回放缓冲区：

class ReplayBuffer:
    def __init__(self, buffer_size):
        self.buffer_size = buffer_size
        self.buffer = []

    def add(self, state, action, reward, next_state, done):
        experience = (state, action, reward, next_state, done)
        if len(self.buffer) >= self.buffer_size:
            self.buffer.pop(0)
        self.buffer.append(experience)

    def sample(self, batch_size):
        batch = np.random.choice(self.buffer, batch_size)
        states = np.array([experience[0] for experience in batch])
        actions = np.array([experience[1] for experience in batch])
        rewards = np.array([experience[2] for experience in batch])
        next_states = np.array([experience[3] for experience in batch])
        dones = np.array([experience[4] for experience in batch])
        return states, actions, rewards, next_states, dones

接下来，我们定义DDPG的Agent：

class DDPGAgent:
    def __init__(self, state_dim, action_dim, action_bound, buffer_size, batch_size, tau, gamma, actor_learning_rate, critic_learning_rate):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.action_bound = action_bound
        self.buffer_size = buffer_size
        self.batch_size = batch_size
        self.tau = tau
        self.gamma = gamma
        self.actor_learning_rate = actor_learning_rate
        self.critic_learning_rate = critic_learning_rate

        self.actor_network = ActorNetwork(self.state_dim, self.action_dim, self.action_bound, self.actor_learning_rate, self.tau)
        self.critic_network = CriticNetwork(self.state_dim, self.action_dim, self.critic_learning_rate, self.tau)

        self.target_actor_network = ActorNetwork(self.state_dim, self.action_dim, self.action_bound, self.actor_learning_rate, self.tau)
        self.target_critic_network = CriticNetwork(self.state_dim, self.action_dim, self.critic_learning_rate, self.tau)

        self.replay_buffer = ReplayBuffer(self.buffer_size)

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

        self.update_target_networks()

    def update_target_networks(self):
        actor_network_params = self.actor_network.network_params
        target_actor_network_params = self.target_actor_network.network_params
        self.sess.run([tf.assign(target_actor_network_params[i], actor_network_params[i]) for i in range(len(actor_network_params))])

        critic_network_params = self.critic_network.network_params
        target_critic_network_params = self.target_critic_network.network_params
        self.sess.run([tf.assign(target_critic_network_params[i], critic_network_params[i]) for i in range(len(critic_network_params))])

    def get_action(self, state):
        action = self.sess.run(self.actor_network.scaled_outputs, feed_dict={self.actor_network.inputs: state})
        return action[0]

    def train(self):
        states, actions, rewards, next_states, dones = self.replay_buffer.sample(self.batch_size)

        target_actions = self.sess.run(self.target_actor_network.scaled_outputs, feed_dict={self.target_actor_network.inputs: next_states})
        target_q_values = self.sess.run(self.target_critic_network.outputs, feed_dict={self.target_critic_network.inputs: next_states, self.target_critic_network.action: target_actions})

        targets = []
        for i in range(self.batch_size):
            if dones[i]:
                targets.append(rewards[i])
            else:
                targets.append(rewards[i] + self.gamma * target_q_values[i])

        self.sess.run(self.critic_network.train_op, feed_dict={self.critic_network.inputs: states, self.critic_network.action: actions, self.critic_network.outputs: np.reshape(targets, [self.batch_size, 1])})

        actions_for_gradients = self.sess.run(self.actor_network.scaled_outputs, feed_dict={self.actor_network.inputs: states})
        gradients = self.sess.run(self.critic_network.gradients, feed_dict={self.critic_network.inputs: states, self.critic_network.action: actions_for_gradients})

        self.sess.run(self.actor_network.train_op, feed_dict={self.actor_network.inputs: states, self.actor_network.action_gradients: gradients[0]})

        self.update_target_networks()

    def add_experience(self, state, action, reward, next_state, done):
        self.replay_buffer.add(state, action, reward, next_state, done)

最后，我们可以使用DDPGAgent来训练我们的模型：

# 定义环境维度和动作维度
state_dim = 4
action_dim = 2
action_bound = 1

# 创建DDPGAgent
agent = DDPGAgent(state_dim, action_dim, action_bound, buffer_size=10000, batch_size=32, tau=0.001, gamma=0.99, actor_learning_rate=0.001, critic_learning_rate=0.001)

# 进行训练
for episode in range(1000):
    state = env.reset()
    total_reward = 0
    done = False

    while not done:
        action = agent.get_action(state)
        next_state, reward, done, _ = env.step(action)
        agent.add_experience(state, action, reward, next_state, done)
        agent.train()

        state = next_state
        total_reward += reward

    print("Episode:", episode, "Total Reward:", total_reward)

# 使用训练好的模型进行测试
state = env.reset()
done = False

while not done:
    action = agent.get_action(state)
    next_state, reward, done, _ = env.step(action)
    state = next_state
    env.render()

请注意，上述代码中的环境环节需要根据你的实际情况进行修改，以适应你的问题。

希望这个简单的DDPG案例能帮到你！如果有任何问题，请随时提问。