aspg-18-3778-1792515290 20261020165450 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 17 1 K. Rajeswari Research scholar, Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India B. Arun kumar Professor and Head, Department of Artificial Intelligence and Data Science, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India In vehicle ad hoc networks (VANETs), vehicles often need to perform complex computing tasks that may exceed their processing capabilities within the required period to provide enhanced services. A common approach to improving service performance is to offload tasks to roadside units (RSUs). However, RSUs might not always have sufficient resources to manage all task assignments effectively. Given the increasing processing power of modern vehicles, task delegation to other vehicles presents a viable alternative to relying solely on RSUs. To achieve this, we first introduce a probabilistic approach that relaxes discrete actions, such as cloud server selection, into a continuous space. We then implement a Supportive Multi-Agent Deep Reinforcement Learning (SMADRL) technique that minimizes total system costs, including Vehicle device energy consumption and cloud server rental charges, by utilizing a centralized training and distributed execution approach. In this framework, each Vehicle device operates as an independent agent, learning efficient decentralized policies that reduce computing pressure on the devices. Experimental results show that the proposed SMADRL framework effectively learns dynamic offloading policies for each Vehicle device and notably outperforms four state-of-the-art DRL-based agents and two heuristic frameworks, resulting in reduce overall system costs. 2025 75 88 3778 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170106 https://www.americaspg.com/journal/18/article/3778 https://www.americaspg.com/storage/01744652203.pdf