Sumit Mukhopadhay
Papers
1
Total Citations
3
H-Index
1
About
Sumit Mukhopadhay is a researcher in artificial intelligence and robotics, with a focus on reinforcement learning and autonomous systems. His work centers on applying machine learning algorithms to enable intelligent decision-making in mobile robots, particularly through Q-learning—a model-free reinforcement learning technique. His most cited paper, "Application of single agent Q-learning for light exploration" (2010), demonstrates how a single agent can autonomously learn to navigate and explore environments by responding to light stimuli, a foundational step toward adaptive robotic behavior. Though his citation count is modest, this early contribution highlights his role in advancing practical, algorithm-driven robotics. Mukhopadhay’s research bridges theoretical machine learning and real-world robotic applications, offering insights into how agents can evolve behaviors from environmental data. His work is particularly relevant for students and researchers interested in reinforcement learning, autonomous navigation, and the integration of AI into physical systems, providing a clear example of how Q-learning can be applied to solve exploratory tasks in dynamic settings.
Research Focus
Key Achievements
Top Papers
- 1Application of single agent Q-learning for light exploration3 citations · 2010