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Potential-based Reinforcement Learning Combined with Case-based Decision Theory  

Kim, Eun-Sun (서강대학교 컴퓨터공학과)
Chang, Hyeong-Soo (서강대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.15, no.12, 2009 , pp. 978-982 More about this Journal
Abstract
This paper proposes a potential-based reinforcement learning, called "RLs-CBDT", which combines multiple RL agents and case-base decision theory designed for decision making in uncertain environment as an expert knowledge in RL. We empirically show that RLs-CBDT converges to an optimal policy faster than pre-existing RL algorithms through a Tetris experiment.
Keywords
Potential-based RL; Sarsa(0); Case-based Decision Theory; Tetris problem;
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