2 papers
eess.SY2025
Off-Policy Reinforcement Learning with Anytime Safety Guarantees via Robust Safe Gradient Flow
Pol Mestres, Arnau Marzabal, Jorge Cortés
This paper considers the problem of solving constrained reinforcement learning (RL) problems with anytime guarantees, meaning that the algorithmic solution must yield a constraint-…
cs.RO2025
From Space to Time: Enabling Adaptive Safety with Learned Value Functions via Disturbance Recasting
Sander Tonkens, Nikhil Uday Shinde, Azra Begzadić +3
The widespread deployment of autonomous systems in safety-critical environments such as urban air mobility hinges on ensuring reliable, performant, and safe operation under varying…