Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

This paper presents a new Reinforcement Learning (RL)-based control approach that uses
the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the …

This paper introduces a novel reference tracking control approach implemented using a
combination of the Actor-Critic Reinforcement Learning (RL) framework and the Grey Wolf …

This paper considers an optimal control of an affine nonlinear system with unknown system
dynamics. A new identifier–critic framework is proposed to solve the optimal control problem …

The purpose of this study is to utilize reinforcement learning in order to improve the
performance of the Sand Cat Swarm Optimization algorithm (SCSO). In this paper, we …

Aiming at the defects of the sparrow search algorithm (SSA), such as a deficient optimization
accuracy and low search efficiency, the sparrow search algorithm based on quantum …

Even though the unprecedented success of AlphaGo Zero demonstrated reinforcement
learning as a feasible complex problem solver, the research on reinforcement learning …

Control of unstable process is challenging owing to its dynamic nature, output multiplicities
and stability issues. This research work focuses to develop a generic data driven modified …

Clustering is a popular data analysis and data mining problem. Symmetry can be
considered as a pre-attentive feature, which can improve shapes and objects, as well as …

Many engineering and scientific problems in the real-world boil down to optimization
problems, which are difficult to solve by using traditional methods. Meta-heuristics are …