1 citations · 1 across the 15 of their papers we have counts for
5 papers · 1 filter
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment
Claas A Voelcker, Marcel Hussing, Eric Eaton
Empirical, benchmark-driven testing is a fundamental paradigm in the current RL community. While using off-the-shelf benchmarks in reinforcement learning (RL) research is a common…
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
Claas A Voelcker, Marcel Hussing, Eric Eaton +2
Building deep reinforcement learning (RL) agents that find a good policy with few samples has proven notoriously challenging. To achieve sample efficiency, recent work has explored…
Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical video
Guiqiu Liao, Matjaz Jogan, Sai Koushik +2
Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring an extensive training dataset of object masks, relying instead…
Distributed Continual Learning
Long Le, Marcel Hussing, Eric Eaton
This work studies the intersection of continual and federated learning, in which independent agents face unique tasks in their environments and incrementally develop and share know…
Dissecting Deep RL with High Update Ratios: Combatting Value Divergence
Marcel Hussing, Claas Voelcker, Igor Gilitschenski +2
We show that deep reinforcement learning algorithms can retain their ability to learn without resetting network parameters in settings where the number of gradient updates greatly…