Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2008
Abstract
TD-FALCON (Temporal Difference - Fusion Architecture for Learning, COgnition, and Navigation) is a class of self-organizing neural networks that incorporates Temporal Difference (TD) methods for real-time reinforcement learning. In this paper, we present two strategies, i.e. policy sharing and neighboring-agent mechanism, to further improve the learning efficiency of TD-FALCON in complex multi-agent domains. Through experiments on a traffic control problem domain and the herding task, we demonstrate that those strategies enable TD-FALCON to remain functional and adaptable in complex multi-agent domains
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'08), Australia, December 9-12
First Page
326
Last Page
329
Identifier
10.1109/WIIAT.2008.259
Publisher
IEEE
City or Country
New York
Citation
XIAO, Dan and TAN, Ah-hwee.
Scaling up multi-agent reinforcement learning in complex domains. (2008). Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'08), Australia, December 9-12. 326-329.
Available at: https://ink.library.smu.edu.sg/sis_research/6798
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
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