Ryeonggu Kwon
Papers
2
Total Citations
6
H-Index
2
About
Ryeonggu Kwon is a rising researcher at the intersection of artificial intelligence and formal verification, with a primary focus on ensuring the safety of reinforcement learning (RL) and deep reinforcement learning (DRL) systems. His work addresses a critical gap in AI development: while most RL research prioritizes reward maximization, Kwon emphasizes the equally vital need for safety constraints and non-functional requirements in safety-centric applications like autonomous driving and robotics. In his 2024 paper, "Applying Quantitative Model Checking to Analyze Safety in Reinforcement Learning" (4 citations), he pioneers the use of quantitative model checking—a formal verification technique—to systematically analyze and guarantee safety properties in RL policies. Building on this foundation, his 2025 study, "Optimization of State Clustering and Safety Verification in Deep Reinforcement Learning Using KMeans++ and Probabilistic Model Checking" (2 citations), introduces an innovative framework that combines KMeans++ clustering with probabilistic model checking to efficiently verify safety in complex, high-dimensional DRL environments. Although early in his career, Kwon’s contributions are already shaping how researchers approach trustworthy AI, offering scalable methods to prevent catastrophic failures in real-time systems. His work is essential reading for anyone interested in safe, reliable autonomous systems.
Research Focus
Key Achievements
Top Papers
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