most citedTrajectory-Based Off-Policy Deep Reinforcement Learning

1 citations · 1 across the 1 of their papers we have counts for

collaborators

2 papers

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

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…