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Found 4 papers, 2 papers with code
Learning to Act without Actions
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.
Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari
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.
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies
A main theoretical interest in biology and physics is to identify the nonlinear dynamical system (DS) that generated observed time series.
Inferring Dynamical Systems with Long-Range Dependencies through Line Attractor Regularization
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.
