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

arXiv: https://arxiv.org/abs/2101.02178 · HTML: https://mlsanity.com/paper/2101.02178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 10 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Abstract (arXiv)

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.