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We present a technique for speeding up the convergence of value iteration forpartially observable Markov decisions processes (POMDPs). The underlying ideais similar to that behind modified policy iteration for fully observable Markovdecision processes (MDPs). The technique can be easily incorporated into anyexisting POMDP value iteration algorithms. Experiments have been conducted onseveral test problems with one POMDP value iteration algorithm calledincremental pruning. We find that the technique can make incremental pruningrun several orders of magnitude faster.

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