3 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
Safe exploration in reproducing kernel Hilbert spaces
Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön +1
Popular safe Bayesian optimization (BO) algorithms learn control policies for safety-critical systems in unknown environments. However, most algorithms make a smoothness assumption…
PACSBO: Probably approximately correct safe Bayesian optimization
Abdullah Tokmak, Thomas B. Schön, Dominik Baumann
Safe Bayesian optimization (BO) algorithms promise to find optimal control policies without knowing the system dynamics while at the same time guaranteeing safety with high probabi…
Safe reinforcement learning in uncertain contexts
Dominik Baumann, Thomas B. Schön
When deploying machine learning algorithms in the real world, guaranteeing safety is an essential asset. Existing safe learning approaches typically consider continuous variables,…
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations
Dominik Baumann, Erfaun Noorani, James Price +3
Envisioned application areas for reinforcement learning (RL) include autonomous driving, precision agriculture, and finance, which all require RL agents to make decisions in the re…