用扩散强化学习优化自动驾驶车辆任务调度，降低完成时间。
Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning
- 基于扩散模型生成合成经验，提升调度决策效率
- 任务完成时间比基准方案减少约23.5%
- 适合需要实时响应的车联网系统研究者
本文提出一种面向动态无人机辅助连通自动驾驶车辆（CAVs）的依赖感知任务调度策略。针对包含多个依赖子任务的计算任务，合理分配至附近车辆或基站以实现快速完成。为此，构建了联合调度优先级与子任务分配的优化问题，目标是最小化平均任务完成时间，将问题重构成马尔可夫决策过程。为求解该问题，提出一种基于扩散强化学习的算法——合成DDQN子任务调度（Synthetic DDQN-based Subtasks Scheduling），可在实时环境中自适应做出调度决策。该算法在强化学习框架中引入基于扩散模型的合成经验回放机制，能够生成充足合成数据填充经验回放缓冲区，显著加速收敛并提升样本效率。仿真结果表明，所提算法在降低任务完成时间方面优于基准方案。
原文摘要 · Abstract (English)
In this paper, we propose a novel dependency-aware task scheduling strategy for dynamic unmanned aerial vehicle-assisted connected autonomous vehicles (CAVs). Specifically, different computation tasks of CAVs consisting of multiple dependency subtasks are judiciously assigned to nearby CAVs or the base station for promptly completing tasks. Therefore, we formulate a joint scheduling priority and subtask assignment optimization problem with the objective of minimizing the average task completion time. The problem aims at improving the long-term system performance, which is reformulated as a Markov decision process. To solve the problem, we further propose a diffusion-based reinforcement learning algorithm, named Synthetic DDQN based Subtasks Scheduling, which can make adaptive task scheduling decision in real time. A diffusion model-based synthetic experience replay is integrated into the reinforcement learning framework, which can generate sufficient synthetic data in experience replay buffer, thereby significantly accelerating convergence and improving sample efficiency. Simulation results demonstrate the effectiveness of the proposed algorithm on reducing task completion time, comparing to benchmark schemes.
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