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Solving POMDPs by Searching the Space of Finite Policies
[article]
2013
arXiv
pre-print
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, we explore the problem of finding the optimal policy from a restricted set of policies, represented as finite state automata of a given size. This problem is also intractable, but we show that the complexity can be greatly reduced when the POMDP and/or policy are further constrained. We demonstrate good empirical
arXiv:1301.6720v1
fatcat:rq2dryw4frfxba3hkb2chu42k4