7 papers
To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control
Victor Schulte, Michael Eichelbeck, Matthias Althoff
Reinforcement learning (RL) can be a powerful alternative to classical control methods when standard model-based control is insufficient, e.g., when deriving a suitable model is in…
Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks
Laura Lützow, Michael Eichelbeck, Mykel J. Kochenderfer +1
Conformal prediction is a popular uncertainty quantification method that augments a base predictor to return sets of predictions with statistically valid coverage guarantees. Howev…
BSAT: B-Spline Adaptive Tokenizer for Long-Term Time Series Forecasting
Maximilian Reinwardt, Michael Eichelbeck, Matthias Althoff
Long-term time series forecasting using transformers is hampered by the quadratic complexity of self-attention and the rigidity of uniform patching, which may be misaligned with th…
Improving Stochastic Action-Constrained Reinforcement Learning via Truncated Distributions
Roland Stolz, Michael Eichelbeck, Matthias Althoff
In reinforcement learning (RL), it is often advantageous to consider additional constraints on the action space to ensure safety or action relevance. Existing work on such action-c…
CommonPower: A Framework for Safe Data-Driven Smart Grid Control
Michael Eichelbeck, Hannah Markgraf, Matthias Althoff
The growing complexity of power system management has led to an increased interest in reinforcement learning (RL). To validate their effectiveness, RL algorithms have to be evaluat…
PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning Projects
Hannah Markgraf, Michael Eichelbeck, Daria Cappey +3
Offline reinforcement learning (RL) has gained traction as a powerful paradigm for learning control policies from pre-collected data, eliminating the need for costly or risky onlin…