A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2209.03859v1
• Date: Thu, 8 Sep 2022 14:58:50 GMT
• Title: A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning
• Authors: Kai Cui, Anam Tahir, Gizem Ekinci, Ahmed Elshamanhory, Yannick Eich, Mengguang Li, Heinz Koeppl
• Abstract summary: We will shed light on current approaches to tractably understanding and analyzing large-population systems. We will survey potential areas of application for large-scale control and identify fruitful future applications of learning algorithms in practical systems.
• Score: 18.918558716102144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The analysis and control of large-population systems is of great interest to diverse areas of research and engineering, ranging from epidemiology over robotic swarms to economics and finance. An increasingly popular and effective approach to realizing sequential decision-making in multi-agent systems is through multi-agent reinforcement learning, as it allows for an automatic and model-free analysis of highly complex systems. However, the key issue of scalability complicates the design of control and reinforcement learning algorithms particularly in systems with large populations of agents. While reinforcement learning has found resounding empirical success in many scenarios with few agents, problems with many agents quickly become intractable and necessitate special consideration. In this survey, we will shed light on current approaches to tractably understanding and analyzing large-population systems, both through multi-agent reinforcement learning and through adjacent areas of research such as mean-field games, collective intelligence, or complex network theory. These classically independent subject areas offer a variety of approaches to understanding or modeling large-population systems, which may be of great use for the formulation of tractable MARL algorithms in the future. Finally, we survey potential areas of application for large-scale control and identify fruitful future applications of learning algorithms in practical systems. We hope that our survey could provide insight and future directions to junior and senior researchers in theoretical and applied sciences alike.

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