强化学习ddpg解决tsp

强化学习（Reinforcement Learning）是一种通过智能体与环境的交互来学习最优行为的机器学习方法。TSP（Traveling Salesman Problem）是一个NP难问题，即在给定的一些城市和每对城市之间的距离，求解访问每一座城市一次并回到起始城市的最短回路。DDPG（Deep Deterministic Policy Gradient）是一种基于策略梯度的深度强化学习算法，可以用于解决连续动作空间的问题。

因此，可以使用DDPG算法来解决TSP问题。具体来说，可以将每个城市看作一个状态，智能体需要在这些状态之间进行移动，并且需要在访问每个城市后回到起始城市。智能体的目标是最小化访问每个城市的总距离。在DDPG算法中，智能体的策略网络可以输出每个状态下应该采取的动作，而值网络可以评估每个状态下采取动作的价值。通过不断地与环境交互，智能体可以学习到最优的策略，从而解决TSP问题。

下面是一个使用DDPG算法解决TSP问题的代码示例：

import numpy as np
import tensorflow as tf
import gym

# 定义智能体的策略网络和值网络
class Actor:
    def __init__(self, sess, n_features, n_actions, lr=0.001):
        self.sess = sess
        self.s = tf.placeholder(tf.float32, [None, n_features], "state")
        self.a = tf.placeholder(tf.float32, [None, n_actions], "action")
        self.td_error = tf.placeholder(tf.float32, None, "td_error")

        l1 = tf.layers.dense(
            inputs=self.s,
            units=30,
            activation=tf.nn.relu,
            kernel_initializer=tf.random_normal_initializer(0., .1),
            bias_initializer=tf.constant_initializer(0.1),
            name='l1'
        )

        mu = tf.layers.dense(
            inputs=l1,
            units=n_actions,
            activation=tf.nn.tanh,
            kernel_initializer=tf.random_normal_initializer(0., .1),
            bias_initializer=tf.constant_initializer(0.1),
            name='mu'
        )

        sigma = tf.layers.dense(
            inputs=l1,
            units=n_actions,
            activation=tf.nn.softplus,
            kernel_initializer=tf.random_normal_initializer(0., .1),
            bias_initializer=tf.constant_initializer(0.1),
            name='sigma'
        )

        global_step = tf.Variable(0, trainable=False)
        self.mu, self.sigma = tf.squeeze(mu*2), tf.squeeze(sigma+0.1)
        self.normal_dist = tf.distributions.Normal(self.mu, self.sigma)

        self.action = tf.clip_by_value(self.normal_dist.sample(1), -2, 2)

        with tf.name_scope('exp_v'):
            log_prob = self.normal_dist.log_prob(self.a)
            self.exp_v = log_prob * self.td_error
            self.exp_v += 0.01*self.normal_dist.entropy()

        with tf.name_scope('train'):
            self.train_op = tf.train.AdamOptimizer(lr).minimize(-self.exp_v, global_step=global_step)

    def learn(self, s, a, td):
        self.sess.run(self.train_op, {self.s: s, self.a: a, self.td_error: td})

    def choose_action(self, s):
        return self.sess.run(self.action, {self.s: s})

class Critic:
    def __init__(self, sess, n_features, lr=0.01):
        self.sess = sess
        self.s = tf.placeholder(tf.float32, [None, n_features], "state")
        self.v_ = tf.placeholder(tf.float32, [None, 1], "v_next")
        self.r = tf.placeholder(tf.float32, None, 'r')

        l1 = tf.layers.dense(
            inputs=self.s,
            units=30,
            activation=tf.nn.relu,
            kernel_initializer=tf.random_normal_initializer(0., .1),
            bias_initializer=tf.constant_initializer(0.1),
            name='l1'
        )

        self.v = tf.layers.dense(
            inputs=l1,
            units=1,
            activation=None,
            kernel_initializer=tf.random_normal_initializer(0., .1),
            bias_initializer=tf.constant_initializer(0.1),
            name='V'
        )

        with tf.name_scope('squared_TD_error'):
            self.td_error = self.r + 0.9*self.v_ - self.v
            self.loss = tf.square(self.td_error)
        with tf.name_scope('train'):
            self.train_op = tf.train.AdamOptimizer(lr).minimize(self.loss)

    def learn(self, s, r, s_):
        v_ = self.sess.run(self.v, {self.s: s_})
        td_error, _ = self.sess.run([self.td_error, self.train_op],
                                    {self.s: s, self.v_: v_, self.r: r})
        return td_error

# 定义环境
class TSPEnv(gym.Env):
    def __init__(self, n_cities):
        self.n_cities = n_cities
        self.cities = np.random.rand(n_cities, 2)
        self.distances = np.zeros((n_cities, n_cities))
        for i in range(n_cities):
            for j in range(n_cities):
                self.distances[i][j] = np.sqrt(np.sum(np.square(self.cities[i] - self.cities[j])))
        self.reset()

    def reset(self):
        self.visited = np.zeros(self.n_cities)
        self.current_city = np.random.randint(self.n_cities)
        self.visited[self.current_city] = 1
        self.total_distance = 0
        self.step_count = 0
        return self.get_state()

    def get_state(self):
        state = np.zeros((self.n_cities, 3))
        for i in range(self.n_cities):
            state[i][0] = self.visited[i]
            state[i][1] = self.distances[self.current_city][i]
            state[i][2] = self.total_distance
        return state.flatten()

    def step(self, action):
        self.step_count += 1
        next_city = np.argmax(action)
        if self.visited[next_city] == 1:
            reward = -10
        else:
            reward = -self.distances[self.current_city][next_city]
            self.visited[next_city] = 1
            self.current_city = next_city
            self.total_distance += self.distances[self.current_city][next_city]
        done = (self.step_count == self.n_cities)
        return self.get_state(), reward, done, {}

# 训练智能体
def train(sess, env, actor, critic):
    for i_episode in range(1000):
        state = env.reset()
        total_reward = 0
        while True:
            action = actor.choose_action(state[np.newaxis, :])
            state_, reward, done, _ = env.step(action)
            td_error = critic.learn(state[np.newaxis, :], reward, state_[np.newaxis, :])
            actor.learn(state[np.newaxis, :], action, td_error)
            state = state_
            total_reward += reward
            if done:
                break
        print('Episode:', i_episode, 'Total reward:', total_reward)

# 测试智能体
def test(sess, env, actor):
    state = env.reset()
    while True:
        action = actor.choose_action(state[np.newaxis, :])
        state_, reward, done, _ = env.step(action)
        state = state_
        if done:
            break
    print('Total distance:', env.total_distance)

# 创建环境和智能体
env = TSPEnv(10)
sess = tf.Session()
actor = Actor(sess, env.observation_space.shape[0], env.action_space.n)
critic = Critic(sess, env.observation_space.shape[0])

# 初始化变量
sess.run(tf.global_variables_initializer())

# 训练智能体
train(sess, env, actor, critic)

# 测试智能体
test(sess, env, actor)