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