226 citations · 603 across the 11 of their papers we have counts for
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cs.AI2013★ 188 cited
Learning Finite-State Controllers for Partially Observable Environments
Nicolas Meuleau, Leonid Peshkin, Kee-Eung Kim +1
Reactive (memoryless) policies are sufficient in completely observable Markov decision processes (MDPs), but some kind of memory is usually necessary for optimal control of a parti…
cs.AI2013★ 148 cited
Solving POMDPs by Searching the Space of Finite Policies
Nicolas Meuleau, Kee-Eung Kim, Leslie Pack Kaelbling +1
Solving partially observable Markov decision processes (POMDPs) is highly intractable in general, at least in part because the optimal policy may be infinitely large. In this paper…