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Forward-Backward Sweep Method for the System of HJB-FP Equations in Memory-Limited Partially Observable Stochastic Control [PDF]
Entropy, 2023 Memory-limited partially observable stochastic control (ML-POSC) is the stochastic optimal control problem under incomplete information and memory limitation.
Takehiro Tottori, Tetsuya J. Kobayashi
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Memory-Limited Partially Observable Stochastic Control and Its Mean-Field Control Approach [PDF]
Entropy, 2022 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 ...
Takehiro Tottori, Tetsuya J. Kobayashi
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Decentralized Stochastic Control with Finite-Dimensional Memories: A Memory Limitation Approach [PDF]
Entropy, 2023 Decentralized stochastic control (DSC) is a stochastic optimal control problem consisting of multiple controllers. DSC assumes that each controller is unable to accurately observe the target system and the other controllers.
Takehiro Tottori, Tetsuya J. Kobayashi
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Advances in Neural Information Processing Systems 35, 2022 Control problems are always challenging since they arise from the real-world systems where stochasticity and randomness are of ubiquitous presence. This naturally and urgently calls for developing efficient neural control policies for stabilizing not only the deterministic equations but the stochastic systems as well.
Jingdong Zhang 0001 +2 more
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Decentralized stochastic control [PDF]
Annals of Operations Research, 2014 Decentralized stochastic control refers to the multi-stage optimization of a dynamical system by multiple controllers that have access to different information. Decentralization of information gives rise to new conceptual challenges that require new solution approaches.
Aditya Mahajan, Mehnaz Mannan
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Control mechanisms for stochastic biochemical systems via computation of reachable sets [PDF]
Royal Society Open Science, 2017 Controlling the behaviour of cells by rationally guiding molecular processes is an overarching aim of much of synthetic biology. Molecular processes, however, are notoriously noisy and frequently nonlinear.
Eszter Lakatos, Michael P. H. Stumpf
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Active Inference for Stochastic Control [PDF]
, 2021 12 pages, 5 figures, Accepted presentation at IWAI-2021 (ECML-PKDD)
Aswin Paul +3 more
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A Semi-Markov Dynamic Capital Injection Problem for Distressed Banks
Risks, 2023 Our study investigates the optimal dividend strategy for a bank, taking into account the potential for government capital injections. We explore different types of government interventions, such as liberal, transparent, or uncertain strategies, and ...
Luca Di Persio +2 more
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Optimal lock-down intensity: A stochastic pandemic control approach of path integral
Computational and Mathematical Biophysics, 2023 The aim of this article is to determine the optimal intensity of lock-down measures and vaccination rates to control the spread of coronavirus disease 2019. The study uses a stochastic susceptible-infected-recovered (SIR) model with infection dynamics. A
Pramanik Paramahansa
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Cross Apprenticeship Learning Framework: Properties and Solution Approaches
IEEE Open Journal of Control Systems, 2023 Apprenticeship learning is a framework in which an agent learns a policy to perform a given task in an environment using example trajectories provided by an expert. In the real world, one might have access to expert trajectories in different environments
Ashwin Aravind +2 more
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