no code implementations • 23 Feb 2024 • Alessandro G. Bottero, Carlos E. Luis, Julia Vinogradska, Felix Berkenkamp, Jan Peters
In this paper, we propose an information-theoretic safe exploration criterion that directly exploits the GP posterior to identify the most informative safe parameters to evaluate.
no code implementations • 7 Dec 2023 • Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters
Previous work upper bounds the posterior variance over values by solving a so-called uncertainty Bellman equation (UBE), but the over-approximation may result in inefficient exploration.
no code implementations • 1 Dec 2023 • Petru Tighineanu, Lukas Grossberger, Paul Baireuther, Kathrin Skubch, Stefan Falkner, Julia Vinogradska, Felix Berkenkamp
Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution.
no code implementations • 12 Aug 2023 • Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters
We study the problem from a model-based Bayesian reinforcement learning perspective, where the goal is to learn the posterior distribution over value functions induced by parameter (epistemic) uncertainty of the Markov decision process.
1 code implementation • 24 Feb 2023 • Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters
We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning.
1 code implementation • 9 Dec 2022 • Alessandro G. Bottero, Carlos E. Luis, Julia Vinogradska, Felix Berkenkamp, Jan Peters
We consider a sequential decision making task where we are not allowed to evaluate parameters that violate an a priori unknown (safety) constraint.
1 code implementation • 22 Nov 2021 • Petru Tighineanu, Kathrin Skubch, Paul Baireuther, Attila Reiss, Felix Berkenkamp, Julia Vinogradska
Bayesian optimization is a powerful paradigm to optimize black-box functions based on scarce and noisy data.
1 code implementation • 7 Feb 2020 • Lukas P. Fröhlich, Edgar D. Klenske, Julia Vinogradska, Christian Daniel, Melanie N. Zeilinger
We consider the problem of robust optimization within the well-established Bayesian optimization (BO) framework.