強化学習における次元削減

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The topic of this research is dimension reduction in reinforcement learning. Our achievements so far are three publications.1. The first publication appeared in Neural Networks journal, vol. 84, pages 1-16, December 2016. In this work we developed a model-based reinforcement learning method with dimension reduction. We collaborate with the Brain-Computer Interface department at ATR institute in Kyoto and experimentally evaluate the proposed method on a full-size humanoid robot.2. The second publication appeared in the proceedings of AAAI conference on Artificial Intelligence, February 2017. In this work we develop model-based contextual reinforcement learning method with dimension reduction. We collaborate with the Autonomous Intelligent Systems laboratory at Technical University of Darmstadt in Germany and experimentally evaluate the proposed method using a simulated robot arm.3. The third publication is accepted for publication by the Neural Computation journal on 21st March 2017. In this work, we presented preliminary results on dimension reduction method based on quadratic mutual information in the supervised learning setting.