3 papers
cs.LG2025
Scaling CrossQ with Weight Normalization
Daniel Palenicek, Florian Vogt, Jan Peters
Reinforcement learning has achieved significant milestones, but sample efficiency remains a bottleneck for real-world applications. Recently, CrossQ has demonstrated state-of-the-a…
cs.LG2025
Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
Daniel Palenicek, Florian Vogt, Joe Watson +1
Reinforcement learning has achieved significant milestones, but sample efficiency remains a bottleneck for real-world applications. Recently, CrossQ has demonstrated state-of-the-a…
cs.LG2024
Diminishing Return of Value Expansion Methods
Daniel Palenicek, Michael Lutter, João Carvalho +3
Model-based reinforcement learning aims to increase sample efficiency, but the accuracy of dynamics models and the resulting compounding errors are often seen as key limitations. T…