no code implementations • 7 May 2024 • Berian James, Stefan Pollok, Ignacio Peis, Jes Frellsen, Rasmus Bjørk
We present a generative model that amortises computation for the field around e. g. gravitational or magnetic sources.
1 code implementation • 14 Mar 2022 • Stefan Pollok, Nataniel Olden-Jørgensen, Peter Stanley Jørgensen, Rasmus Bjørk
By minimizing this statistical distance, a reconstruction loss as well as physical losses, our trained generator has learned to predict the missing field values with a median reconstruction test error of 5. 14%, when a single coherent region of field points is missing, and 5. 86%, when only a few point measurements in space are available and the field measurements around are predicted.
no code implementations • 5 Feb 2021 • Julian Bernhard, Stefan Pollok, Alois Knoll
Specifically, we first learn an optimal policy in an uncertain environment with Deep Distributional Reinforcement Learning.
Distributional Reinforcement Learning Reinforcement Learning (RL)