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…
cs.LG2025
Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization
Pierre-François Massiani, Alexander von Rohr, Lukas Haverbeck +1
Despite the many recent advances in reinforcement learning (RL), the question of learning policies that robustly satisfy state constraints under unknown disturbances remains open.…
cs.RO2024
Latent Action Priors for Locomotion with Deep Reinforcement Learning
Oliver Hausdörfer, Alexander von Rohr, Éric Lefort +1
Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unrestricted nature of the learning algori…