Multi-Agent Reinforcement Learning for task allocation in the Internet of Vehicles: Exploring benefits and paving the future

The Internet of Vehicles (IoV) and its applications are undergoing massive development, requiring diverse autonomous or self-directed vehicles/agents to fulfill various objective and responsibilities in vehicular technology. Similarly, Multi-Agent Systems (MAS) and multi-agent task allocation are currently the main focus of multiple researchers and scholars, and they play a key role in IoV and the Internet of Things (IoT). The development of the IoV and autonomous vehicles plays a significant role in Intelligent Transportation Systems (ITS), which are empowered by vehicular networks . However, the dynamic nature of these networks presents substantial challenges that need to be addressed. In this regard, we trace the historical evolution of the multi-agent task allocation of IoV, highlight its fundamentals and progress, and discuss the existing survey works. This paper comprehensively reviews various IoV strategies, both multi-agent task allocation strategies and Multi-Agent Reinforcement Learning (MARL), emphasizing the intelligent learning architecture, concepts, and security-related issues. Additionally, we highlight various computing platforms and the diverse applications of multi-agent task allocation in IoV, where task allocation is challenging and presents security concerns of multi-agent task allocation in IoV. Finally, we discuss major open problems regarding multi-agent task allocation scalability, complexity, communication overhead , resource allocation , security, privacy, etc., and potential future perspectives on multi-agent task allocation methods are highlighted.

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