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- W2287992105 abstract "The partially observable Markov decision process (POMDP) provides a principled general model for planning under uncertainty. However, solving a general POMDP is computationally intractable in the worst case. This paper introduces POMDP-lite, a subclass of POMDPs in which the hidden state variables are constant or only change deterministically. We show that a POMDP-lite is equivalent to a set of fully observable Markov decision processes indexed by a hidden parameter and is useful for modeling a variety of interesting robotic tasks. We develop a simple model-based Bayesian reinforcement learning algorithm to solve POMDP-lite models. The algorithm performs well on large-scale POMDP-lite models with up to 1020 states and outperforms the state-of-the-art general-purpose POMDP algorithms. We further show that the algorithm is near-Bayesian-optimal under suitable conditions." @default.
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- W2287992105 date "2016-05-01" @default.
- W2287992105 modified "2023-10-14" @default.
- W2287992105 title "POMDP-lite for robust robot planning under uncertainty" @default.
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- W2287992105 doi "https://doi.org/10.1109/icra.2016.7487754" @default.
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