Abstract: In this paper, a new data-based Q-learning algorithm is proposed to address the optimal control issue for a class of discrete-time switched affine systems (SASs). The algorithm shifts the ...

Artificial Intelligence (AI) has undergone remarkable progress over the past few decades, and one of the most transformative areas within this field is Reinforcement Learning (RL). Unlike supervised ...

Abstract: Successive Over-Relaxation Q-learning (SOR-QL) has been proposed recently as an alternative to the widely popular Q-learning algorithm as it is seen to provide better performance where ...

This important study uses reinforcement learning to study how turbulent odor stimuli should be processed to yield successful navigation. The authors find that there is an optimal memory length over ...

Reinforcement Learning (RL) is no longer just a research curiosity—it’s the engine behind game-changing advances in robotics, autonomous systems, and intelligent control. But with so many algorithms ...

In today’s world, sustainable development is a major priority, and the combination of renewable energy sources with microgrids has revolutionized energy distribution systems. Microgrids, with their ...

This paper investigates the Dynamic Flexible Job Shop Scheduling Problem (DFJSP), which is based on new job insertion, machine breakdowns, changes in processing time, and considering the state of ...