نویسنده
Saldı, Naci, Yuksel, S., Linder, T.
تاریخ انتشار
2020-01
محل انتشار
-
IEEE
موضوع
Aerospace electronics, Convergence, Quantization (signal), Markov processes, Computational modeling, Cost function, Approximations, Markov decision processes, Non-linear filtering, Quantization, Stochastic control
نوع
دوره ای
زبان
انگلیسی
دیجیتال
بله
نسخه خطی
خیر
کتابخانه
دانشگاه اوزیغین
شناسه دارایی کتابخانه
0018-9286
شماره ثبت
9f33c013-e783-4804-b33d-1fa2896875a9
محل کتابخانه
Natural and Mathematical Sciences
تاریخ
2020-01
یادداشتها
Natural Sciences and Engineering Research Council of Canada (NSERC)
متن نمونه
We consider finite model approximations of discrete-time partially observed Markov decision processes (POMDPs) under the discounted cost criterion. After converting the original partially observed stochastic control problem to a fully observed one on the belief space, the finite models are obtained through the uniform quantization of the state and action spaces of the belief space Markov decision process (MDP). Under mild assumptions on the components of the original model, it is established that the policies obtained from these finite models are nearly optimal for the belief space MDP, and so, for the original partially observed problem. The assumptions essentially require that the belief space MDP satisfies a mild weak continuity condition. We provide an example and introduce explicit approximation procedures for the quantization of the set of probability measures on the state space of POMDP (i.e., belief space).
DOI
10.1109/TAC.2019.2907172
Cilt
65
