2 papers
cs.LG2026
Boundary-Seeking Policy Gradient for Safe Reinforcement Learning
Chenhua Fan, Jiahui Zhu, Yuhang Zhang +1
Safe reinforcement learning maximizes reward subject to safety constraints. For Constrained Markov Decision Processes, the linear-programming view over occupancy measures implies t…
cs.LG2026
SteinGate: Tail-Sensitive Safe Reinforcement Learning via Stein Discrepancy
Yassine Chemingui, Chenhua Fan, Honghao Wei +1
SteinGate introduces a distributional safety certificate based on Kernelized Stein Discrepancy to detect rare, high-cost tail events in reinforcement learning and dynamically switc…