Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning
This work addresses task scheduling for CAVs to improve efficiency in dynamic environments, representing an incremental advance in optimization methods for this domain.
The paper tackles the problem of scheduling computation tasks for connected autonomous vehicles (CAVs) by proposing a dependency-aware strategy that assigns subtasks to nearby CAVs or a base station to minimize average task completion time, with simulation results showing effectiveness in reducing this time compared to benchmarks.
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.