18 citations · 21 across the 21 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…
Oracle-Efficient Reinforcement Learning for Max Value Ensembles
Marcel Hussing, Michael Kearns, Aaron Roth +2
Reinforcement learning (RL) in large or infinite state spaces is notoriously challenging, both theoretically (where worst-case sample and computational complexities must scale with…
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…