@inproceedings{
    pajarinen11c,
    author = "Pajarinen, Joni and Peltonen, Jaakko",
    language = "eng",
    title = "{Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning}",
    booktitle = "Proceedings of NIPS 2011, the 25th Annual Conference on Neural Information Processing Systems",
    corerank = "A*",
    unitcode = "T306=99,T312=1",
    month = "December",
    responsibleauthor = "Pajarinen, Joni and Peltonen, Jaakko",
    flags = "AIRC HIIT private",
    il = "no",
    year = "2011",
    pdf = "pajarinen11_nips.pdf",
    impactfactor = "A4",
    abstract = "Applications such as robot control and wireless communication require planning under uncertainty. Partially observable Markov decision processes (POMDPs) plan policies for single agents under uncertainty and their decentralized versions (DEC-POMDPs) find a policy for multiple agents. The policy in infinite-horizon POMDP and DEC-POMDP problems has been represented as finite state controllers (FSCs). We introduce a novel class of periodic FSCs, composed of layers connected only to the previous and next layer. Our periodic FSC method finds a deterministic finite-horizon policy and converts it to an initial periodic infinite-horizon policy. This policy is optimized by a new infinite-horizon algorithm to yield deterministic periodic policies, and by a new expectation maximization algorithm to yield stochastic periodic policies. Our method yields better results than earlier planning methods and can compute larger solutions than with regular FSCs."
}