no code implementations • 30 Apr 2024 • Robert McCarthy, Daniel C. H. Tan, Dominik Schmidt, Fernando Acero, Nathan Herr, Yilun Du, Thomas G. Thuruthel, Zhibin Li
This includes a discussion of the exciting benefits LfV methods can offer (e. g., improved generalization beyond the available robot data) and commentary on key LfV challenges (e. g., challenges related to missing information in video and LfV distribution shifts).
1 code implementation • 17 Dec 2023 • Dominik Schmidt, Minqi Jiang
LAPO takes a first step towards pre-training powerful, generalist policies and world models on the vast amounts of videos readily available on the web.
1 code implementation • 19 Nov 2021 • Dominik Schmidt, Thomas Schmied
This paper's contribution is threefold: We (1) propose an improved version of Rainbow, seeking to drastically reduce Rainbow's data, training time, and compute requirements while maintaining its competitive performance; (2) we empirically demonstrate the effectiveness of our approach through experiments on the Arcade Learning Environment, and (3) we conduct a number of ablation studies to investigate the effect of the individual proposed modifications.
no code implementations • ICLR 2021 • Dominik Schmidt, Georgia Koppe, Zahra Monfared, Max Beutelspacher, Daniel Durstewitz
A main theoretical interest in biology and physics is to identify the nonlinear dynamical system (DS) that generated observed time series.
no code implementations • 25 Sep 2019 • Dominik Schmidt, Georgia Koppe, Max Beutelspacher, Daniel Durstewitz
Vanilla RNN with ReLU activation have a simple structure that is amenable to systematic dynamical systems analysis and interpretation, but they suffer from the exploding vs. vanishing gradients problem.