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Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning
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DOI：10.1109/TNSE.2025.3636287.png)
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Abstract
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In this paper, we investigate a dependency-aware task scheduling problem in connected autonomous vehicle (CAV) networks. Specifically, each CAV task consists of multiple dependent subtasks, which can be distributed to nearby vehicles or roadside unit for processing. Since frequent subtasks scheduling may increase communication overhead, a scheduling scheme that simplifies task dependencies is designed, incorporating a subtask merging mechanism to reduce the complexity of dependent task scheduling. We formulate a long-term joint subtask scheduling and resource allocation optimization problem to minimize the average tasks completion delay while guaranteeing system stability. Therefore, Lyapunov optimization is utilized to decouple the long-term problem as a multiple instantaneous deterministic problem. To capture the dynamics of vehicular environment and randomness of task arrivals, the problem is reformulated as a parameterized action Markov decision process. To overcome the issue that inefficient exploration of single-step deterministic policies in sparse reward, we propose a novel diffusion-based hybrid proximal policy optimization algorithm, integrating the diffusion model with deep reinforcement learning. Instead of relying on the original policy network, diffusion policy is used to generate continuous actions, which aims to improve the expressiveness of the policy in capturing multimodal action distributions and enhancing decision-making over long horizons through multi-step refinement. Extensive simulation results demonstrate that the proposed algorithm can reduce task completion delay by 6.9%–12.1% compared to state-of-the-art benchmarks.
Keywords:
CAV networks
dependent task scheduling
deep reinforcement learning
diffusion policy
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