no code implementations • 8 May 2024 • Eline M. Bovy, Marnix Suilen, Sebastian Junges, Nils Jansen
Partially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known.
1 code implementation • 13 May 2023 • Patrick Wienhöft, Marnix Suilen, Thiago D. Simão, Clemens Dubslaff, Christel Baier, Nils Jansen
In an offline reinforcement learning setting, the safe policy improvement (SPI) problem aims to improve the performance of a behavior policy according to which sample data has been generated.
no code implementations • 10 Mar 2023 • Thom Badings, Thiago D. Simão, Marnix Suilen, Nils Jansen
In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty.
no code implementations • 12 Jan 2023 • Thiago D. Simão, Marnix Suilen, Nils Jansen
In our novel approach to the SPI problem for POMDPs, we assume that a finite-state controller (FSC) represents the behavior policy and that finite memory is sufficient to derive optimal policies.
1 code implementation • 31 May 2022 • Marnix Suilen, Thiago D. Simão, David Parker, Nils Jansen
Markov decision processes (MDPs) are formal models commonly used in sequential decision-making.
no code implementations • 29 Jan 2021 • Thom S. Badings, Arnd Hartmanns, Nils Jansen, Marnix Suilen
We study a smart grid with wind power and battery storage.
no code implementations • 24 Sep 2020 • Murat Cubuktepe, Nils Jansen, Sebastian Junges, Ahmadreza Marandi, Marnix Suilen, Ufuk Topcu
(3) We linearize this dual problem and (4) solve the resulting finite linear program to obtain locally optimal solutions to the original problem.