An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility

Schmidt LM, Brosig J, Plinge A, Eskofier B, Mutschler C (2022)

Publication Type: Conference contribution

Publication year: 2022

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2022-October

Pages Range: 1342-1349

Conference Proceedings Title: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Event location: Macau, CHN

ISBN: 9781665468800

DOI: 10.1109/ITSC55140.2022.9922205

Abstract

Many scenarios in mobility and traffic involve multiple different agents that need to cooperate to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning to find effective and performant behavior strategies. However, as autonomous vehicles and vehicle-to-X communications become more mature, solutions that only utilize single, independent agents leave potential performance gains on the road. Multi-Agent Reinforcement Learning (MARL) is a research field aiming to find optimal solutions for multiple agents interacting with each other. This work gives an overview of the field to researchers in autonomous mobility. We first explain MARL and introduce important concepts. Then, we discuss the central paradigms that underlie MARL algorithms and give an overview of state-of-the-art methods and ideas in each paradigm. With this background, we survey applications of MARL in autonomous mobility scenarios and give an overview of existing scenarios and implementations.

Authors with CRIS profile

Björn Eskofier Machine Learning and Data Analytics Lab

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Schmidt, L.M., Brosig, J., Plinge, A., Eskofier, B., & Mutschler, C. (2022). An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (pp. 1342-1349). Macau, CHN: Institute of Electrical and Electronics Engineers Inc..

MLA:

Schmidt, Lukas M., et al. "An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility." Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022, Macau, CHN Institute of Electrical and Electronics Engineers Inc., 2022. 1342-1349.

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