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math.OC2025
Quantizer Design for Finite Model Approximations, Model Learning, and Quantized Q-Learning for MDPs with Unbounded Spaces
Osman Bicer, Ali D. Kara, Serdar Yuksel
In this paper, for Markov decision processes (MDPs) with unbounded state spaces we present refined upper bounds presented in [Kara et. al. JMLR'23] on finite model approximation er…
math.OC2025
Kernel Metrics and Learning for Borel MDPs: Identifiability and Adaptive Control
Omar Mrani-Zentar, Serdar Yüksel
We consider a Markov decision process with standard Borel spaces and an unknown transition kernel under the average cost criterion. We do not impose any parametrization on the set…
eess.SY2025
Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning
Naci Saldi, Serdar Yuksel
We introduce a novel topology, called Kernel Mean Embedding Topology, for stochastic kernels, in a weak and strong form. This topology, defined on the spaces of Bochner integrable…