Author
Saldı, Naci, Yuksel, S., Linder, T.
Publication Date
2020-01
Publication Place
-
IEEE
Subject
Aerospace electronics, Convergence, Quantization (signal), Markov processes, Computational modeling, Cost function, Approximations, Markov decision processes, Non-linear filtering, Quantization, Stochastic control
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0018-9286
Record ID
9f33c013-e783-4804-b33d-1fa2896875a9
Library Location
Natural and Mathematical Sciences
Date
2020-01
Notes
Natural Sciences and Engineering Research Council of Canada (NSERC)
Sample Text
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
