ArticleEntropy (Basel, Switzerland)2022
Memory-Limited Partially Observable Stochastic Control and Its Mean-Field Control Approach.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Understanding Nash epidemics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Rational social distancing policy during epidemics with limited healthcare capacity.PLoS computational biology · 2023Article
- Olfactory search with finite-state controllers.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Decentralized Stochastic Control with Finite-Dimensional Memories: A Memory Limitation Approach.Entropy (Basel, Switzerland) · 2023Article
- Forward-Backward Sweep Method for the System of HJB-FP Equations in Memory-Limited Partially Observable Stochastic Control.Entropy (Basel, Switzerland) · 2023Article
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2 authors.
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Abstract
Control problems with incomplete information and memory limitation appear in many practical situations. Although partially observable stochastic control (POSC) is a conventional theoretical framework that considers the optimal control problem with incomplete information, it cannot consider memory limitation. Furthermore, POSC cannot be solved in practice except in special cases. In order to address these issues, we propose an alternative theoretical framework, memory-limited POSC (ML-POSC). ML-POSC directly considers memory limitation as well as incomplete information, and it can be solved in practice by employing the technique of mean-field control theory. ML-POSC can generalize the linear-quadratic-Gaussian (LQG) problem to include memory limitation. Because estimation and control are not clearly separated in the LQG problem with memory limitation, the Riccati equation is modified to the partially observable Riccati equation, which improves estimation as well as control. Furthermore, we demonstrate the effectiveness of ML-POSC for a non-LQG problem by comparing it with the local LQG approximation.
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