A Method for Speeding Up Value Iteration in Partially Observable Markov
  Decision Processes
  We present a technique for speeding up the convergence of value iteration for
partially observable Markov decisions processes (POMDPs). The underlying idea
is similar to that behind modified policy iteration for fully observable Markov
decision processes (MDPs). The technique can be easily incorporated into any
existing POMDP value iteration algorithms. Experiments have been conducted on
several test problems with one POMDP value iteration algorithm called
incremental pruning. We find that the technique can make incremental pruning
run several orders of magnitude faster.
