3 papers
cs.LG2026
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
Mingxuan Che, Tsung-Yuan Tseng, Theresa Eimer +2
Reinforcement learning (RL) has shown remarkable success across a wide range of complex tasks. However, RL outcomes can be highly stochastic, and both expected performance and vari…
eess.SY2024
Practical Considerations for Discrete-Time Implementations of Continuous-Time Control Barrier Function-Based Safety Filters
Lukas Brunke, Siqi Zhou, Mingxuan Che +1
Safety filters based on control barrier functions (CBFs) have become a popular method to guarantee safety for uncertified control policies, e.g., as resulting from reinforcement le…
eess.SY2023
Optimized Control Invariance Conditions for Uncertain Input-Constrained Nonlinear Control Systems
Lukas Brunke, Siqi Zhou, Mingxuan Che +1
Providing safety guarantees for learning-based controllers is important for real-world applications. One approach to realizing safety for arbitrary control policies is safety filte…