This work introduces a decision-making framework for partially observable systems in continuous time with discrete state and action spaces. The authors employ approximation methods to scale well with increasing numbers of states, demonstrating effectiveness on various systems. They propose a new scalable algorithm that divides into two parts: approximating the filtering distribution and adapting a historical heuristic to continuous-time POMDPs.
The content discusses the challenges of partial observability in dynamical systems and presents Bayesian filtering as a solution. It explores optimal control strategies, historical settings, and modern deep learning techniques applied to discrete-time problems. The article highlights the limitations of existing approaches and proposes a novel method based on entropic matching for approximate inference in continuous-time POMDPs.
The experiments section evaluates the proposed method on queueing networks, predator-prey systems, and chemical reaction networks. It showcases the effectiveness of the approach through comparisons with benchmark methods like particle filters. Results demonstrate improved stability and performance in controlled trajectories compared to uncontrolled scenarios.
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Önemli Bilgiler Şuradan Elde Edildi
by Yannick Eich... : arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.01431.pdfDaha Derin Sorular