Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2021
Abstract
We address the problem of multiagent credit assignment in a large scale multiagent system. Difference rewards (DRs) are an effective tool to tackle this problem, but their exact computation is known to be challenging even for small number of agents. We propose a scalable method to compute difference rewards based on aggregate information in a multiagent system with large number of agents by exploiting the symmetry present in several practical applications. Empirical evaluation on two multiagent domains—air-traffic control and cooperative navigation, shows better solution quality than previous approaches.
Keywords
Reinforcement learning, Multiagent systems
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Online, May 3-7
First Page
1655
Last Page
1657
Publisher
IFAAMAS
City or Country
United Kingdom
Citation
SINGH, Arambam James and KUMAR, Akshat.
Approximate difference rewards for scalable multigent reinforcement learning. (2021). Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Online, May 3-7. 1655-1657.
Available at: https://ink.library.smu.edu.sg/sis_research/6901
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.