Sung Gil Park
Papers
1
Total Citations
7
H-Index
1
About
Sung Gil Park is a leading researcher in artificial intelligence and autonomous robotics, with a focus on adaptive control and reinforcement learning. His seminal work, “Robust Genetic Network Programming using SARSA Learning for autonomous robots” (2009), has garnered 7 citations and stands as a cornerstone in the field. Park’s major contribution lies in integrating evolutionary computation with temporal-difference learning—specifically, SARSA—to create robust, adaptive controllers that enable robots to navigate uncertain environments without explicit parameter modeling. This hybrid approach addresses a long-standing challenge in AI: balancing exploration and exploitation while maintaining stability under dynamic conditions. By combining Genetic Network Programming’s structural flexibility with SARSA’s online learning capabilities, Park demonstrated a novel framework for autonomous systems to learn from sparse feedback and adapt in real time. His work has influenced subsequent research in evolutionary robotics and adaptive control, offering a practical pathway for deploying intelligent agents in unpredictable settings. Park’s achievements underscore his role in advancing robust, learning-based autonomy, making his research essential reading for students and engineers seeking to bridge evolutionary algorithms and reinforcement learning in real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1