Ghanbari Ahmad
Papers
1
Total Citations
10
H-Index
1
About
Dr. Ahmad Ghanbari is a researcher whose work lies at the intersection of reinforcement learning and neural networks, with a particular focus on bridging adaptive optimal control and bio-inspired learning. His most-cited paper, "Reinforcement Learning in Neural Networks: A Survey" (2014, 10 citations), provides a comprehensive overview of neural network reinforcement learning (NNRL) algorithms, highlighting their role in advancing autonomous decision-making systems. This survey has served as a foundational resource for researchers exploring how neural networks can enhance RL frameworks. Dr. Ghanbari’s contributions are especially relevant to the growing field of intelligent control systems, where his work helps translate theoretical advances into practical applications. While his citation count reflects a focused, emerging impact, his survey remains a valuable entry point for students and researchers seeking to understand the integration of neural networks with reinforcement learning. His research continues to influence developments in adaptive control and bio-inspired computational models.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement Learning in Neural Networks: A Survey10 citations · 2014