Cooperation and Fairness in Multi-Agent Reinforcement Learning

Cooperation and Fairness in Multi-Agent Reinforcement Learning

Multi-agent systems are trained to maximize shared cost objectives, which typically reflect system-level efficiency. However, in the resource-constrained environments of mobility and transportation systems, efficiency may be achieved at the expense of fairness --- certain agents may incur significan...

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Bibliographic Details
Main Authors: Aloor, Jasmine, Nayak, Siddharth Nagar, Dolan, Sydney, Balakrishnan, Hamsa
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Format: Article
Language:English
Published: ACM 2024
Online Access:https://hdl.handle.net/1721.1/157544

Internet

https://hdl.handle.net/1721.1/157544

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