learning agents, optimal behavior, dynamic joint action perception
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). All Faculty Publications. 304.
Physical and Mathematical Sciences
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