Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning

TL;DR

This paper introduces a diffusion-based reinforcement learning approach for dependency-aware task scheduling in UAV-assisted connected autonomous vehicles, significantly reducing task completion time.

Contribution

It proposes a novel diffusion-based RL algorithm with synthetic experience replay for real-time, dependency-aware task scheduling in CAVs, improving efficiency over existing methods.

Findings

Reduces average task completion time in simulations.

Accelerates convergence with synthetic experience replay.

Outperforms benchmark scheduling schemes.

Abstract

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.