Proposal of PSwithEFP and its Evaluation in Multi-Agent Reinforcement Learning
Kazuteru Miyazaki, Koudai Furukawa, Hiroaki Kobayashi, National Institution for Academic Degrees and Quality Enhancement of Higher Education 1-29-1 Gakuennishimachi, Kodaira, Tokyo 185-8587, Japan · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2017
When multiple agents learn a task simultaneously in an environment, the learning results often become unstable. This problem is known as the concurrent learning problem and to date, several methods have been proposed to resolve it. In this paper, we propose a new method that incorporates expected failure probability (EFP) into the action selection strategy to give agents a kind of mutual adaptability. The effectiveness of the proposed method is confirmed using Keepaway task.