Keywords
learning agents, optimal behavior, dynamic joint action perception
Abstract
Groups of reinforcement learning agents interacting in a common environment often fail to learn optimal behaviors. Poor performance is particularly common in environments where agents must coordinate with each other to receive rewards and where failed coordination attempts are penalized. This paper studies the effectiveness of the Dynamic Joint Action Perception (DJAP) algorithm on a grid-world rendezvous task with this characteristic. The effects of learning rate, exploration strategy, and training time on algorithm effectiveness are discussed. An analysis of the types of tasks for which DJAP learning is appropriate is also presented.
Original Publication Citation
Nancy Fulda and Dan Ventura, "Learning a Rendezvous Task with Dynamic Joint Action Perception", Proceedings of the International Joint Conference on Neural Networks, pp. 627-632, July 26.
BYU ScholarsArchive Citation
Fulda, Nancy and Ventura, Dan A., "Learning a Rendezvous Task with Dynamic Joint Action Perception" (2006). Faculty Publications. 304.
https://scholarsarchive.byu.edu/facpub/304
Document Type
Peer-Reviewed Article
Publication Date
2006-07-01
Permanent URL
http://hdl.lib.byu.edu/1877/2525
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
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