A Survey of solution techniques for the partially observed Markov decision process
Annals of Operations Research
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Finite-memory control of partially observable systems
Finite-memory control of partially observable systems
A model approximation scheme for planning in partially observable stochastic domains
Journal of Artificial Intelligence Research
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making
Sequence Learning - Paradigms, Algorithms, and Applications
Speeding up the convergence of value iteration in partially observable Markov decision processes
Journal of Artificial Intelligence Research
Nonapproximability results for partially observable Markov decision processes
Journal of Artificial Intelligence Research
An improved grid-based approximation algorithm for POMDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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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.