Md. Rashed Khan
Papers
1
Total Citations
46
H-Index
1
About
Md. Rashed Khan is a prominent researcher at the intersection of artificial intelligence and robotics, with a primary focus on reinforcement learning (RL) and its transformative applications in autonomous systems. His most influential work, "A Systematic Review on Reinforcement Learning-Based Robotics Within the Last Decade" (2020), has garnered 46 citations, establishing a foundational roadmap for integrating RL into robotic control during the Fourth Industrial Revolution. Khan’s major contribution lies in systematically analyzing how RL enables robots to learn complex behaviors through trial-and-error interactions, reducing the need for explicit programming and enhancing adaptability in dynamic environments. This review has become a key reference for researchers developing intelligent robotic systems, from industrial automation to service robotics. Beyond this seminal paper, Khan’s research explores the synergy between deep learning and RL, advancing the field of autonomous decision-making. His work is notable for bridging theoretical RL algorithms with practical robotic implementations, offering clear guidelines for deploying these techniques in real-world scenarios. For students and researchers, Khan’s contributions provide both a comprehensive overview of the field’s evolution and a practical toolkit for designing next-generation autonomous systems, making him a vital voice in modern robotics and AI research.
Research Focus
Key Achievements
Top Papers
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