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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
Safe reinforcement learning typically enforces safety by bounding expected cumulative costs, a criterion that often fails to detect rare but catastrophic tail events. To overcome t…