1 citations · 2 across the 3 of their papers we have counts for
4 papers
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
Matteo Turchetta, Andreas Krause, Sebastian Trimpe
In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment. In real-world a…
Trajectory-Based Off-Policy Deep Reinforcement Learning
Andreas Doerr, Michael Volpp, Marc Toussaint +2
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, aff…
Probabilistic Recurrent State-Space Models
Andreas Doerr, Christian Daniel, Martin Schiegg +4
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) p…
A New Perspective and Extension of the Gaussian Filter
Manuel Wüthrich, Sebastian Trimpe, Daniel Kappler +1
The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filte…