A Method for Speeding Up Value Iteration in Partially Observable Markov\n Decision Processes

We present a technique for speeding up the convergence of value iteration for\npartially observable Markov decisions processes (POMDPs). The underlying idea\nis similar to that behind modified policy iteration for fully observable Markov\ndecision processes (MDPs). The technique can be easily incorporated into any\nexisting POMDP value iteration algorithms. Experiments have been conducted on\nseveral test problems with one POMDP value iteration algorithm called\nincremental pruning. We find that the technique can make incremental pruning\nrun several orders of magnitude faster.\n