DQN求解tsp问题代码

以下是使用DQN算法求解TSP问题的Python代码：

import numpy as np
import tensorflow as tf
import random
import math

# 定义神经网络
class Model:
    def __init__(self, n_cities, n_neurons):
        self.n_cities = n_cities
        self.n_neurons = n_neurons
        self.graph = tf.Graph()
        with self.graph.as_default():
            self.input = tf.placeholder(tf.float32, shape=[None, n_cities, 2])
            self.hidden = tf.layers.dense(tf.reshape(self.input, [-1, n_cities * 2]), n_neurons, activation=tf.nn.relu)
            self.logits = tf.layers.dense(self.hidden, n_cities)
            self.output = tf.nn.softmax(self.logits)
            self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=self.output, logits=self.logits))
            self.optimizer = tf.train.AdamOptimizer().minimize(self.loss)
            self.init = tf.global_variables_initializer()
            self.saver = tf.train.Saver()

    def predict(self, session, X):
        return session.run(self.output, feed_dict={self.input: X})

    def train(self, session, X, Y):
        session.run(self.optimizer, feed_dict={self.input: X, self.output: Y})

    def save(self, session, path):
        self.saver.save(session, path)

    def load(self, session, path):
        self.saver.restore(session, path)

# 定义DQN算法
class DQN:
    def __init__(self, n_cities, n_neurons, n_episodes, batch_size, gamma, epsilon, epsilon_min, epsilon_decay, memory_size):
        self.n_cities = n_cities
        self.n_neurons = n_neurons
        self.n_episodes = n_episodes
        self.batch_size = batch_size
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_min = epsilon_min
        self.epsilon_decay = epsilon_decay
        self.memory_size = memory_size
        self.memory = []
        self.model = Model(n_cities, n_neurons)
        self.target_model = Model(n_cities, n_neurons)
        self.sess = tf.Session(graph=self.model.graph)
        self.sess.run(self.model.init)
        self.sess.run(self.target_model.init)
        self.update_target_model()

    def update_target_model(self):
        self.target_model.load(self.sess, tf.train.latest_checkpoint('./'))

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))
        if len(self.memory) > self.memory_size:
            self.memory.pop(0)

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.randint(self.n_cities)
        else:
            return np.argmax(self.model.predict(self.sess, np.array([state])))

    def replay(self):
        if len(self.memory) < self.batch_size:
            return
        minibatch = random.sample(self.memory, self.batch_size)
        X = np.zeros((self.batch_size, self.n_cities, 2))
        Y = np.zeros((self.batch_size, self.n_cities))
        for i in range(self.batch_size):
            state, action, reward, next_state, done = minibatch[i]
            target = self.model.predict(self.sess, np.array([state]))[0]
            if done:
                target[action] = reward
            else:
                Q_next = np.max(self.target_model.predict(self.sess, np.array([next_state]))[0])
                target[action] = reward + self.gamma * Q_next
            X[i] = state
            Y[i] = target
        self.model.train(self.sess, X, Y)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

    def run(self, cities):
        state = np.array(cities)
        total_reward = 0
        done = False
        while not done:
            action = self.act(state)
            next_state, reward, done = self.step(state, action)
            self.remember(state, action, reward, next_state, done)
            state = next_state
            total_reward += reward
            self.replay()
        return total_reward

    def step(self, state, action):
        next_state = np.copy(state)
        next_state[:, 0], next_state[:, action] = next_state[:, action], next_state[:, 0]
        reward = -np.sum(np.sqrt(np.sum(np.diff(next_state, axis=0) ** 2, axis=1)))
        done = np.array_equal(next_state[:, 0], np.arange(self.n_cities))
        return next_state, reward, done

    def train(self, cities):
        for episode in range(self.n_episodes):
            random.shuffle(cities)
            reward = self.run(cities)
            print('Episode: {}, Reward: {}, Epsilon: {}'.format(episode, reward, self.epsilon))
            if episode % 10 == 0:
                self.update_target_model()
                self.model.save(self.sess, './model.ckpt')

# 定义TSP问题
class TSP:
    def __init__(self, n_cities):
        self.n_cities = n_cities
        self.cities = np.random.rand(n_cities, 2)

    def distance(self, i, j):
        return np.sqrt(np.sum((self.cities[i] - self.cities[j]) ** 2))

    def reward(self, tour):
        return -np.sum([self.distance(tour[i], tour[i + 1]) for i in range(self.n_cities - 1)]) - self.distance(tour[0], tour[-1])

    def optimal_tour(self):
        return np.argsort([self.distance(i, j) for i in range(self.n_cities) for j in range(i + 1, self.n_cities)])[:self.n_cities]

# 运行DQN算法
n_cities = 10
n_neurons = 128
n_episodes = 1000
batch_size = 32
gamma = 0.99
epsilon = 1.0
epsilon_min = 0.01
epsilon_decay = 0.995
memory_size = 100000
tsp = TSP(n_cities)
dqn = DQN(n_cities, n_neurons, n_episodes, batch_size, gamma, epsilon, epsilon_min, epsilon_decay, memory_size)
dqn.train(tsp.optimal_tour())