6 citations · 6 across the 14 of their papers we have counts for
6 papers · 1 filter
Forecasting in Offline Reinforcement Learning for Non-stationary Environments
Suzan Ece Ada, Georg Martius, Emre Ugur +1
Offline Reinforcement Learning (RL) provides a promising avenue for training policies from pre-collected datasets when gathering additional interaction data is infeasible. However,…
Offline vs. Online Learning in Model-based RL: Lessons for Data Collection Strategies
Jiaqi Chen, Ji Shi, Cansu Sancaktar +2
Data collection is crucial for learning robust world models in model-based reinforcement learning. The most prevalent strategies are to actively collect trajectories by interacting…
Test-time Offline Reinforcement Learning on Goal-related Experience
Marco Bagatella, Mert Albaba, Jonas Hübotter +2
Foundation models compress a large amount of information in a single, large neural network, which can then be queried for individual tasks. There are strong parallels between this…
A Smooth Analytical Formulation of Collision Detection and Rigid Body Dynamics With Contact
Onur Beker, Nico Gürtler, Ji Shi +4
Generating intelligent robot behavior in contact-rich settings is a research problem where zeroth-order methods currently prevail. A major contributor to the success of such method…
SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models
Cansu Sancaktar, Christian Gumbsch, Andrii Zadaianchuk +2
Exploration is a cornerstone of reinforcement learning (RL). Intrinsic motivation attempts to decouple exploration from external, task-based rewards. However, established approache…
Advancing Out-of-Distribution Detection via Local Neuroplasticity
Alessandro Canevaro, Julian Schmidt, Mohammad Sajad Marvi +3
In the domain of machine learning, the assumption that training and test data share the same distribution is often violated in real-world scenarios, requiring effective out-of-dist…