Abstract: Dynamic programming (DP) and reinforcement learning (RL) are effective techniques for maximizing control in uncertain and dynamic systems. In order to ...

1 Department of Statistics and Mathematics, Bindura University of Science Education, Bindura, Zimbabwe 2 Department of Mathematics, University of Botswana, Gaborone, Botswana This study develops a ...

Abstract: This study proposes a model-free robust control method designed by Event-Triggered Incremental Adaptive Dynamic Programming (ET-IADP) scheme for zero-sum game guidance, which reduces the ...

Genomics is playing an important role in transforming healthcare. Genetic data, however, is being produced at a rate that far outpaces Moore’s Law. Many efforts have been made to accelerate genomics ...

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...

1 State Grid Jiangxi Electric Power Co., Ltd., Nanchang, China 2 State Key Laboratory of Advanced Electromagnetic Technology, Huazhong University of Science and Technology, Wuhan, China Large-scale ...

The refrain may feel repetitive, but it can’t be overstated: woefully inadequate transmission supply in the U.S. is colliding with the imperative to deploy renewable energy resources. There is a ...

What is Dynamic Programming page defines optimal substructure as follows: The problem has an "optimal substructure" - an optimal solution can be formed from optimal solutions to the overlapping ...