Research progress of partially observable Markov decision processes
Min Wu · Jisuanji gongcheng yu sheji · 2007
Partially observable Markov decision processes(POMDP) changes the non Markovian into Markovian over the belief state space.It has been an important branch of stochastic decision processes for its characteristics of describing the real world.At first,the principles and decision processes of POMDP is described,then three typical algorithms is presented,including Littman,et al's witness algorithm,incremental pruning algorithm and Pineau,et al's point-based value iteration algorithm,and the results from each of algorithms are analyzed and compared.At last,some applications are introduced using POMDP.