Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls. - Jeongho Kim 002; Jaeuk Shin; Insoon Yang (J. Mach. Learn. Res., 2021)

Infusing Model Predictive Control Into Meta-Reinforcement Learning for Mobile Robots in Dynamic Environments. - Jaeuk Shin; Astghik Hakobyan; Mingyu Park; Yeoneung Kim; Gihun Kim; Insoon Yang (IEEE Robotics Autom. Lett., 2022)

On Task-Relevant Loss Functions in Meta-Reinforcement Learning and Online LQR. - Jaeuk Shin; Giho Kim; Howon Lee; Joonho Han; Insoon Yang (CoRR, 2023)

On task-relevant loss functions in meta-reinforcement learning. - Jaeuk Shin; Giho Kim; Howon Lee; Joonho Han; Insoon Yang (L4DC, 2024)

Anderson acceleration for partially observable Markov decision processes: A maximum entropy approach. - Mingyu Park; Jaeuk Shin; Insoon Yang (Autom., 2024)

Multiparametric Analysis of Multi-Task Markov Decision Processes: Structure, Invariance, and Reducibility. - Jaeuk Shin; Insoon Yang (IEEE Control. Syst. Lett., 2024)

Control of Fab Lifters via Deep Reinforcement Learning: A Semi-MDP Approach. - Giho Kim; Jaeuk Shin; Gihun Kim; Joonrak Kim; Insoon Yang (IEEE Trans Autom. Sci. Eng., 2024)

Egocentric Conformal Prediction for Safe and Efficient Navigation in Dynamic Cluttered Environments. - Jaeuk Shin; Jungjin Lee; Insoon Yang (CoRR, 2025)