From the 1 of 8 linked papers with an AI index.
8 papers
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
Mingxuan Che, Tsung-Yuan Tseng, Theresa Eimer +2
The paper introduces ERAHBO, a Bayesian optimization approach that models both the mean and variance of reinforcement learning performance with respect to hyperparameters, aiming t…
Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
Henrik Hose, Paul Brunzema, Alexander von Rohr +3
Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a neural network, removing the need to solve an expensive optimization problem at runtime. Howeve…
scipy.spatial.transform: Differentiable Framework-Agnostic 3D Transformations in Python
Martin Schuck, Alexander von Rohr, Angela P. Schoellig
Three-dimensional rigid-body transforms, i.e. rotations and translations, are central to modern differentiable machine learning pipelines in robotics, vision, and simulation. Howev…
Local Entropy Search over Descent Sequences for Bayesian Optimization
David Stenger, Armin Lindicke, Alexander von Rohr +1
Searching large and complex design spaces for a global optimum can be infeasible and unnecessary. A practical alternative is to iteratively refine the neighborhood of an initial de…
Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization
Pierre-François Massiani, Alexander von Rohr, Lukas Haverbeck +1
Despite the many recent advances in reinforcement learning (RL), the question of learning policies that robustly satisfy state constraints under unknown disturbances remains open.…
Diffusion Predictive Control with Constraints
Ralf Römer, Alexander von Rohr, Angela P. Schoellig
Diffusion models have become popular for policy learning in robotics due to their ability to capture high-dimensional and multimodal distributions. However, diffusion policies are…