Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2021
Abstract
We address the problem ofmultiagent 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
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021): May 3-7, Virtual
First Page
1655
Last Page
1657
Identifier
10.5555/3463952.3464191
Publisher
IFAAMS
City or Country
Richland, SC
Embargo Period
7-8-2021
Citation
SINGH, Arambam James; KUMAR, Akshat; and LAU, Hoong Chuin.
Approximate difference rewards for scalable multigent reinforcement learning. (2021). Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021): May 3-7, Virtual. 1655-1657.
Available at: https://ink.library.smu.edu.sg/sis_research/6022
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.5555/3463952.3464191
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons