21 citations · 42 across the 5 of their papers we have counts for
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cs.LG2019★ 1 cited
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…
stat.ML2019★ 10 cited
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer +4
Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization. Readily available algorithms are typ…