234 citations · 938 across the 65 of their papers we have counts for
8 papers · 1 filter
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning
Moritz Schneider, Robert Krug, Narunas Vaskevicius +2
Visual Reinforcement Learning (RL) methods often require extensive amounts of data. As opposed to model-free RL, model-based RL (MBRL) offers a potential solution with efficient da…
BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design
Yannick Vogt, Mehdi Naouar, Maria Kalweit +4
Antibodies offer great potential for the treatment of various diseases. However, the discovery of therapeutic antibodies through traditional wet lab methods is expensive and time-c…
Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington +7
Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive…
Latent Linear Quadratic Regulator for Robotic Control Tasks
Yuan Zhang, Shaohui Yang, Toshiyuki Ohtsuka +2
Model predictive control (MPC) has played a more crucial role in various robotic control tasks, but its high computational requirements are concerning, especially for nonlinear dyn…
PlanNetX: Learning an Efficient Neural Network Planner from MPC for Longitudinal Control
Jasper Hoffmann, Diego Fernandez, Julien Brosseit +5
Model predictive control (MPC) is a powerful, optimization-based approach for controlling dynamical systems. However, the computational complexity of online optimization can be pro…
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control
Yuan Zhang, Jasper Hoffmann, Joschka Boedecker
Learning-based techniques have become popular in both model predictive control (MPC) and reinforcement learning (RL). Probabilistic ensemble (PE) models offer a promising approach…