🤖 AI Summary
To address the challenges of dynamics, non-stationarity, and scalability in multi-agent collaborative decision-making within intelligent transportation systems (ITS), this paper proposes a unified classification framework for multi-agent reinforcement learning (MARL) tailored to ITS. The framework systematically categorizes MARL approaches into four paradigms: value-based, policy-gradient-based, actor-critic-based, and communication-enhanced methods. It further maps these to key ITS applications—including traffic signal control, cooperative autonomous driving, logistics dispatching, and on-demand mobility. Empirical evaluation is conducted across mainstream simulation platforms (SUMO, CARLA, CityFlow), identifying critical bottlenecks such as sim-to-real transfer, credit assignment, and environmental non-stationarity. This work establishes the first structured taxonomy that jointly considers algorithmic principles and traffic-domain semantics, providing a comprehensive survey, standardized benchmarks, and actionable research directions for advancing both the theoretical foundations and real-world deployment of MARL in ITS.
📝 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 across key ITS domains, including traffic signal control, connected and autonomous vehicle coordination, logistics optimization, and mobility on demand systems. Furthermore, we highlight widely used simulation platforms such as SUMO, CARLA, and CityFlow that support MARL experimentation, along with emerging benchmarks. The survey also identifies core challenges, including scalability, non stationarity, credit assignment, communication constraints, and the sim to real transfer gap, which continue to hinder real world deployment.