基于DeepLearning4j框架, 根据已有的三位坐标点组成的路径训练Deep Q-Network (DQN)模型, 然后给出起止点的坐标,自动生成路径点坐标, 给出实现代码

好的，以下是一个使用 DeepLearning4j 框架训练 DQN 模型的示例代码：

import org.deeplearning4j.rl4j.learning.sync.qlearning.discrete.QLearning;
import org.deeplearning4j.rl4j.learning.sync.qlearning.discrete.QLearningDiscreteDense;
import org.deeplearning4j.rl4j.network.dqn.DQN;
import org.deeplearning4j.rl4j.network.dqn.DQNFactoryStdDense;
import org.deeplearning4j.rl4j.space.DiscreteSpace;
import org.nd4j.linalg.learning.config.Adam;

public class PathPlanner {
    public static void main(String[] args) {
        // 定义 Q-Learning 的超参数
        final int seed = 123;
        final int maxStep = 100;
        final int numEpisodes = 1000;
        final double gamma = 0.99;
        final double learningRate = 0.001;
        final int updateStart = 10;
        final int updateInterval = 1;
        final int targetUpdateInterval = 100;
        final int batchSize = 32;

        // 定义动作空间和状态空间
        final int numActions = 4; // 上下左右四个方向
        final int[] shape = {3}; // 三个坐标点
        final DiscreteSpace actionSpace = new DiscreteSpace(numActions);
        final PathStateSpace stateSpace = new PathStateSpace(shape);

        // 定义 DQN 网络架构
        final DQNFactoryStdDense.Configuration netConf = DQNFactoryStdDense.Configuration.builder()
                .l2(0.001).updater(new Adam(learningRate)).numHiddenNodes(100).build();

        // 定义 DQN 模型
        final DQN dqn = netConf.buildDQN(stateSpace.getObservationShape(), actionSpace.getSize());

        // 定义 Q-Learning 算法
        final QLearning.QLConfiguration qlConf = new QLearning.QLConfiguration(seed, maxStep, numEpisodes, gamma,
                learningRate, updateStart, updateInterval, targetUpdateInterval, batchSize);

        // 定义学习环境
        final PathEnvironment env = new PathEnvironment();

        // 训练 DQN 模型
        final QLearningDiscreteDense<PathState> dql = new QLearningDiscreteDense<>(env, dqn, qlConf);
        dql.train();

        // 使用 DQN 模型生成路径
        final PathState startState = new PathState(new double[]{0, 0, 0});
        final PathState endState = new PathState(new double[]{5, 5, 5});
        final PathState currentState = startState;
        final double[] path = new double[9];
        int pathIndex = 0;
        while (!currentState.equals(endState) && pathIndex < 9) {
            final int action = dqn.argmax(currentState.toArray());
            final double[] nextStateArray = env.getNextStateArray(currentState.toArray(), action);
            final PathState nextState = new PathState(nextStateArray);
            path[pathIndex++] = nextStateArray[0];
            path[pathIndex++] = nextStateArray[1];
            path[pathIndex++] = nextStateArray[2];
            currentState = nextState;
        }

        // 打印生成的路径
        System.out.println("Path: ");
        for (int i = 0; i < pathIndex; i += 3) {
            System.out.println("(" + path[i] + ", " + path[i + 1] + ", " + path[i + 2] + ")");
        }
    }
}

需要注意的是，这只是一个简单的示例代码，您需要根据您的具体需求进行修改和优化。