Newest papers

Near Optimal Finite Model POMDP Approximation of Controlled Diffusion with Partial Information

Yunus Emre Demirci, Somnath Pradhan, Serdar Yüksel · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28377 · open-access PDF

Japanese

我々の主な貢献は次のとおりである: (i) 我々は近い最適性を示し、限られた地平線の下で、および割引された無限の地平線のコスト基準を示し、分散した時間に部分的に観察可能なマルコフ決定プロセス(POMDP)のアプローチへのソリューションを介して得られた断片的に恒久的なコントロールポリシーの近い最適性を示す; (ii) 我々は、このようなPOMDPの近似モデルのいくつかの規則性属性を取得し、 (iii) これらの規則性属性を用いて、我々は

Machine translation (Meta M2M100); the English original follows.

Abstract

We study continuous-time stochastic control problems under partial information. Our main contributions are the following: (i) We show near optimality, under finite horizon as well as discounted infinite horizon cost criteria, of piece-wise constant control policies obtained via solutions to discrete-time partially observable Markov decision process (POMDP) approximations, constructed via Euler-Maruyama approximations of both state and observation processes; (ii) we obtain several regularity properties of such POMDP approximate models; and (iii) using these regularity properties, we arrive at n

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange