Newest papers

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

Yunus Emre Demirci, Somnath Pradhan, Serdar Yüksel · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28377 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

我们的主要贡献如下:(i)我们显示近乎优化,在有限的地平线下,以及折扣无限的地平线成本标准,通过解决方案获得的零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零零

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 near optimal finite model approximations. (iv) These culminate into our main result which is that an optimal solution for the approximate finite POMDP model is near optimal for the original diffusion with partial information.

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

Topics: Partially observable Markov decision process, Optimal control, Mathematics, Observable, Markov process, Applied mathematics

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