Results 11 to 20 of about 159,913 (265)
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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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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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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A direct approach to linear-quadratic stochastic control [PDF]
Opuscula Mathematica, 2017 A direct approach is used to solve some linear-quadratic stochastic control problems for Brownian motion and other noise processes. This direct method does not require solving Hamilton-Jacobi-Bellman partial differential equations or backward stochastic ...
Tyrone E. Duncan, Bozenna Pasik-Duncan
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IEEE Access, 2021 In this article, a robust decentralized tracking control scheme for a large-scale unmanned aerial vehicle (UAV) formation team networked control system (NCS) is proposed to overcome a non-scalable or even infeasible design problem due to high ...
Min-Yen Lee +3 more
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Algorithms, 2020 In this paper we study the optimization of the discrete-time stochastic linear-quadratic (LQ) control problem with conic control constraints on an infinite horizon, considering multiplicative noises. Stochastic control systems can be formulated as Markov
Ruobing Xue, Xiangshen Ye, Weiping Wu
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Towards Tensor Representation of Controlled Coupled Markov Chains
Mathematics, 2020 For a controlled system of coupled Markov chains, which share common control parameters, a tensor description is proposed. A control optimality condition in the form of a dynamic programming equation is derived in tensor form.
Daniel McInnes +3 more
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