Multi-agent reinforcement learning for autonomous vehicles: a survey

• Published in: Autonomous intelligent systems 2022-11, Vol.2 (1), p.1-12, Article 27
• Main Authors: Dinneweth, Joris, Boubezoul, Abderrahmane, Mandiau, René, Espié, Stéphane
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Automation; Autonomous cars; Autonomous Vehicles; Control and Systems Theory; Decision making; Driver behavior; Engineering; Engineering Sciences; Heterogeneity; Humans and Autonomous Entities in Sustainable Intelligent Transportation Solutions; Intelligent systems; Learning; Machine Learning; Multi-agent reinforcement learning; Multiagent systems; Review; Robotics and Automation; Simulation; Taxonomy; Traffic flow; Traffic safety; Unmanned aerial vehicles
• ISSN: 2730-616X; 2730-616X
• DOI: 10.1007/s43684-022-00045-z

In the near future, autonomous vehicles (AVs) may cohabit with human drivers in mixed traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual mobility, as well as from the road safety point of view. Mixed traffic may fail to fulfill expected security requirements due to the heterogeneity and unpredictability of human drivers, and autonomous cars could then monopolize the traffic. Using multi-agent reinforcement learning (MARL) algorithms, researchers have attempted to design autonomous vehicles for both scenarios, and this paper investigates their recent advances. We focus on articles tackling decision-making problems and identify four paradigms. While some authors address mixed traffic problems with or without social-desirable AVs, others tackle the case of fully-autonomous traffic. While the latter case is essentially a communication problem, most authors addressing the mixed traffic admit some limitations. The current human driver models found in the literature are too simplistic since they do not cover the heterogeneity of the drivers’ behaviors. As a result, they fail to generalize over the wide range of possible behaviors. For each paper investigated, we analyze how the authors formulated the MARL problem in terms of observation, action, and rewards to match the paradigm they apply.