# Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey

## TL;DR

This survey comprehensively reviews how Multi-Agent Reinforcement Learning is applied to various aspects of Intelligent Transportation Systems, highlighting approaches, applications, tools, and ongoing challenges in the field.

## Contribution

It provides a structured taxonomy of MARL methods in ITS, reviews key applications, and discusses current challenges and simulation platforms for future research.

## Key findings

- MARL approaches are categorized by coordination and learning algorithms.
- Applications span traffic control, autonomous vehicle coordination, and logistics.
- Key challenges include scalability, non-stationarity, and sim-to-real transfer issues.

## 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.