Mohammed Gamal Ragab

Papers

Total Citations

178

H-Index

About

Mohammed Gamal Ragab is a leading researcher in the field of Deep Reinforcement Learning (DRL), with a particular focus on the Deep Deterministic Policy Gradient (DDPG) algorithm. His major contributions include a comprehensive systematic review of DDPG, which has become an essential resource for understanding how this algorithm tackles complex decision-making in high-dimensional state and action spaces. This work has garnered significant attention, with his 2024 review alone accumulating 164 citations, underscoring its impact on the DRL community. Ragab’s research systematically analyzes DDPG’s architecture, applications, and performance, providing critical insights for advancing autonomous systems, robotics, and AI-driven control. His ability to synthesize and clarify complex algorithmic developments has made his work a cornerstone for students and researchers entering the field. By bridging theoretical foundations with practical implementations, Ragab has established himself as a key figure in DRL, shaping how next-generation decision-making algorithms are understood and applied.