[2508.20315] Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey

Computer Science > Machine Learning arXiv:2508.20315 (cs) [Submitted on 27 Aug 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey Authors:Rexcharles Donatus, Kumater Ter, Daniel Udekwe View a PDF of the paper titled Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey, by Rexcharles Donatus and 2 other authors View PDF HTML (experimental) Abstract:The growing complexity of urban mobility and the demand for efficient, sustainable, and adaptive solutions have positioned Intelligent Transportation Systems (ITS) at the forefront of modern infrastructure innovation. At the core of ITS lies the challenge of autonomous decision-making across dynamic, large scale, and uncertain environments where multiple agents traffic signals, autonomous vehicles, or fleet units must coordinate effectively. Multi Agent Reinforcement Learning (MARL) offers a promising paradigm for addressing these challenges by enabling distributed agents to jointly learn optimal strategies that balance individual objectives with system wide efficiency. This paper presents a comprehensive survey of MARL applications in ITS. We introduce a structured taxonomy that categorizes MARL approaches according to coordination models and learning algorithms, spanning value based, policy based, actor critic, and communication enhanced frameworks. Applications are reviewed acro...