Abstract
With the development of multi-access edge computing (MEC), task offloading has become a promising paradigm to help the resource-constrained vehicles with their computation-intensive and time-sensitive tasks. Recently, deep reinforcement learning based methods are used to tackle the task offloading problems in the vehicular networks with multiple MEC servers. Due to the dynamic movements of the vehicles, we address the importance of cooperative resource utilization among servers and vehicles to reduce the task completion latency of the system. Therefore, we propose a multi-agent reinforcement learning approach to perform collaborative decision making for the vehicles in a multi-edge task offloading scenario. Moreover, we design and pre-train a vehicular trajectory prediction module based on Transformer to provide supplemental trajectory information for better collaboration. We compare our proposed model with several other offloading strategies on four simulation scenarios. The results show the effectiveness of our approach in terms of both task latency and resource utilization.
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Acknowledgements
This work is supported in part by the Program of Technology Innovation of the Science and Technology Commission of Shanghai Municipality (no. 21511104700), National Science Foundation of China (no. 62072304), Shanghai Municipal Science and Technology Commission (no. 21511104700), the Shanghai East Talents Program, the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University (no. SL2020MS032), and Zhejiang Aoxin Co. Ltd.
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Zhang, X., Zhu, Y., Wang, C. et al. Multi-agent reinforcement learning for vehicular task offloading with multi-step trajectory prediction. CCF Trans. Pervasive Comp. Interact. 6, 101–114 (2024). https://doi.org/10.1007/s42486-023-00146-5
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DOI: https://doi.org/10.1007/s42486-023-00146-5