23 citations · 52 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
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.…
Event-Triggered Time-Varying Bayesian Optimization
Paul Brunzema, Alexander von Rohr, Friedrich Solowjow +1
We consider the problem of sequentially optimizing a time-varying objective function using time-varying Bayesian optimization (TVBO). Current approaches to TVBO require prior knowl…
Local policy search with Bayesian optimization
Sarah Müller, Alexander von Rohr, Sebastian Trimpe
Reinforcement learning (RL) aims to find an optimal policy by interaction with an environment. Consequently, learning complex behavior requires a vast number of samples, which can…