Trajectory-Based Off-Policy Deep Reinforcement Learning

2 code implementations • 14 May 2019 • Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel

Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks.

Continuous Control Policy Gradient Methods +3

Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization

2 code implementations • ICLR 2020 • Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner, Frank Hutter, Christian Daniel

Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization.

Bayesian Optimization Gaussian Processes +2

Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds

1 code implementation • NeurIPS 2018 • David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch

Gaussian Processes (GPs) are a generic modelling tool for supervised learning.

Gaussian Processes Generalization Bounds +1

Probabilistic Recurrent State-Space Models

2 code implementations • ICML 2018 • Andreas Doerr, Christian Daniel, Martin Schiegg, Duy Nguyen-Tuong, Stefan Schaal, Marc Toussaint, Sebastian Trimpe

State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.

Gaussian Processes Time Series Analysis +1

Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

no code implementations • 8 Mar 2017 • Andreas Doerr, Duy Nguyen-Tuong, Alonso Marco, Stefan Schaal, Sebastian Trimpe

PID control architectures are widely used in industrial applications.