Preprocessing for Point-Based Algorithms of POMDP
基于点的POMDP算法的预处理方法

Point-Based algorithms are a class of approximation methods for partially observable Markov decision processes(POMDP).They do backup operators on a belief set only,so linear programming is avoided and fewer intermediate variables are needed,and the bottleneck turns from selecting vectors to generating vectors.But when generate vectors,there will be a great deal of repeated and meaningless computing.This paper will propose a preprocessing method for point-based algorithms(PPBA).This method preprocesses each sampled belief point,and before generating a-vectors it estimates which action and a-vectors to be selected f'trst,in so doing repeated computing is eliminated.Base-vector is also defined in this paper,which cancels meaningless computing with sparseness of problem.Experiments on Perseus show that,PPBA accelerates the performance greatly.