Improving Training Result of Partially Observable Markov Decision Process by Filtering Beliefs

Oscar LiJen Hsu

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arXiv · 2101.02178

Improving Training Result of Partially Observable Markov Decision Process by Filtering Beliefs

Oscar LiJen Hsu

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Abstract

In this study I proposed a filtering beliefs method for improving performance of Partially Observable Markov Decision Processes(POMDPs), which is a method wildly used in autonomous robot and many other domains concerning control policy. My method search and compare every similar belief pair. Because a similar belief have insignificant influence on control policy, the belief is filtered out for reducing training time. The empirical results show that the proposed method outperforms the point-based approximate POMDPs in terms of the quality of training results as well as the efficiency of the method.

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Oscar LiJen Hsu. 2021-01-05. Improving Training Result of Partially Observable Markov Decision Process by Filtering Beliefs. https://arxiv.org/abs/2101.02178

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