논문명 | Enhanced Reinforcement Learning by Recursive Updating of Q-values for Reward Propagation |
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논문종류 | SCI |
저자 | Yunsick Sung, Eunyoung Ahn, Kyungeun Cho |
Impact Factor | |
게재학술지명 | Lecture Notes in Electrical Engineering |
게재일 | 2012.12 |
In this paper, we propose a method to reduce the learning time of Q-learning by combining the method of updating even to Q-values of unexecuted actions with the method of adding a terminal reward to unvisited Q-values. To verify the method, its performance was compared to that of conventional Q-learning. The proposed approach showed the same performance as conventional Q-learning, with only 27 % of the learning episodes required for conventional Q-learning. Accordingly, we verified that the proposed method reduced learning time by updating more Q-values in the early stage of learning and distributing a terminal reward to more Q-values. |