用Matlab和python交互通信做一个飞机规避导弹的强化学习训练Matlab中算好一个状态发送给pythonpython接收到此状态后再通过ddpg强化学习算出一个新的动作将此动作返回给Matlab以此作训练。给出具体python和Matlab代码

以下是一个简单的示例代码，仅供参考。

Matlab代码：

% 初始化状态
state = [0 0 0 0];
% 连接Python
python = pyenv('Version', 'C:\Python37\python.exe');
py_ddpg = py.importlib.import_module('ddpg');
% 训练循环
for i = 1:1000
    % 发送状态给Python
    py_state = py.list(state);
    py_action = py_ddpg.get_action(py_state);
    % 将Python返回的动作转换为Matlab数组
    action = double(py_action);
    % 更新状态
    state = update_state(state, action);
end

Python代码：

import matlab.engine
import numpy as np
from ddpg import DDPG

# 连接Matlab
eng = matlab.engine.start_matlab()

# 初始化DDPG
ddpg = DDPG()

# 获取状态并返回动作
def get_action(state):
    state = np.array(state)
    action = ddpg.get_action(state)
    py_action = matlab.double(action.tolist())
    return eng.workspace['py_action'] = py_action

# 关闭Matlab引擎
eng.quit()

其中，DDPG算法的实现可以参考以下Python代码：

import numpy as np
import tensorflow as tf


class DDPG:
    def __init__(self):
        self.sess = tf.Session()
        self.state_dim = 4
        self.action_dim = 2
        self.action_bound = 1
        self.actor_lr = 0.001
        self.critic_lr = 0.002
        self.gamma = 0.99
        self.tau = 0.01
        self.memory_size = 10000
        self.batch_size = 32
        self.memory = np.zeros((self.memory_size, self.state_dim * 2 + self.action_dim + 1))
        self.pointer = 0
        self.actor, self.actor_target, self.actor_optimizer = self.build_actor()
        self.critic, self.critic_target, self.critic_optimizer, self.critic_input = self.build_critic()
        self.sess.run(tf.global_variables_initializer())

    # 构建Actor网络
    def build_actor(self):
        state_input = tf.placeholder(tf.float32, [None, self.state_dim])
        net = tf.layers.dense(state_input, 32, activation=tf.nn.relu)
        net = tf.layers.dense(net, 32, activation=tf.nn.relu)
        action_output = tf.layers.dense(net, self.action_dim, activation=tf.nn.tanh)
        scaled_output = tf.multiply(action_output, self.action_bound)
        trainable_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='actor')
        target_trainable_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='actor_target')
        update_target_ops = [target_var.assign(tf.multiply(var, self.tau) + tf.multiply(target_var, 1 - self.tau)) for var, target_var in zip(trainable_vars, target_trainable_vars)]
        optimizer = tf.train.AdamOptimizer(self.actor_lr)
        action_input = tf.placeholder(tf.float32, [None, self.action_dim])
        q_gradient = tf.placeholder(tf.float32, [None, self.action_dim])
        actor_loss = -tf.reduce_mean(q_gradient * action_output)
        gradients = tf.gradients(actor_loss, trainable_vars)
        grads_and_vars = zip(gradients, trainable_vars)
        actor_optimizer = optimizer.apply_gradients(grads_and_vars)
        return action_output, scaled_output, actor_optimizer

    # 构建Critic网络
    def build_critic(self):
        state_input = tf.placeholder(tf.float32, [None, self.state_dim])
        action_input = tf.placeholder(tf.float32, [None, self.action_dim])
        net1 = tf.layers.dense(state_input, 32, activation=tf.nn.relu)
        net1 = tf.layers.dense(net1, 32, activation=tf.nn.relu)
        net2 = tf.layers.dense(action_input, 32, activation=tf.nn.relu)
        net2 = tf.layers.dense(net2, 32, activation=tf.nn.relu)
        net = tf.concat([net1, net2], axis=-1)
        net = tf.layers.dense(net, 1, activation=None)
        trainable_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='critic')
        target_trainable_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='critic_target')
        update_target_ops = [target_var.assign(tf.multiply(var, self.tau) + tf.multiply(target_var, 1 - self.tau)) for var, target_var in zip(trainable_vars, target_trainable_vars)]
        optimizer = tf.train.AdamOptimizer(self.critic_lr)
        q_target = tf.placeholder(tf.float32, [None, 1])
        td_error = tf.losses.mean_squared_error(q_target, net)
        gradients = optimizer.compute_gradients(td_error, var_list=trainable_vars)
        critic_optimizer = optimizer.apply_gradients(gradients)
        critic_input = [state_input, action_input]
        return net, net, critic_optimizer, critic_input

    # 记忆回放
    def store_transition(self, state, action, reward, next_state):
        transition = np.hstack((state, action, [reward], next_state))
        index = self.pointer % self.memory_size
        self.memory[index, :] = transition
        self.pointer += 1

    # Actor更新
    def actor_learn(self, state):
        action = self.sess.run(self.actor, feed_dict={self.actor_input: state})
        q_gradient = self.sess.run(self.critic_input, feed_dict={self.critic_input[0]: state, self.critic_input[1]: action})[0]
        self.sess.run(self.actor_optimizer, feed_dict={self.actor_input: state, self.q_gradient: q_gradient})

    # Critic更新
    def critic_learn(self):
        if self.pointer > self.memory_size:
            sample_index = np.random.choice(self.memory_size, size=self.batch_size)
        else:
            sample_index = np.random.choice(self.pointer, size=self.batch_size)
        batch_memory = self.memory[sample_index, :]
        next_action = self.sess.run(self.actor_target, feed_dict={self.actor_target_input: batch_memory[:, -self.state_dim:]})
        q_target = self.sess.run(self.critic_target, feed_dict={self.critic_input[0]: batch_memory[:, :self.state_dim], self.critic_input[1]: next_action})
        q_target = batch_memory[:, self.state_dim + 1][:, np.newaxis] + self.gamma * q_target
        self.sess.run(self.critic_optimizer, feed_dict={self.critic_input[0]: batch_memory[:, :self.state_dim], self.critic_input[1]: batch_memory[:, self.state_dim:self.state_dim+self.action_dim], self.q_target: q_target})

    # 选择动作
    def get_action(self, state):
        action = self.sess.run(self.actor_target, feed_dict={self.actor_target_input: state})
        noise = np.random.normal(0, 0.1, size=self.action_dim)
        action = np.clip(action + noise, -self.action_bound, self.action_bound)
        return action

    # 更新目标网络
    def update_target(self):
        self.sess.run(self.update_target_ops)
``