• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.978-982
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• 2009

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.