9 papers · 1 filter
Behavior-Consistent Deep Reinforcement Learning
Marcel Hussing, Liv G. d'Aliberti, Claas Voelcker +2
Reinforcement learning (RL) often exhibits high variance across training runs, leading to unreliable performance and posing a major challenge to deployment in real-world domains. I…
Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights
Liangzu Peng, Uday Kiran Reddy Tadipatri, Ziqing Xu +2
Continual learning (CL) is concerned with learning multiple tasks sequentially without forgetting previously learned tasks. Despite substantial empirical advances over recent years…
Model Agreement via Anchoring
Eric Eaton, Surbhi Goel, Marcel Hussing +4
Numerous lines of aim to control -- the extent to which two machine learning models disagree in their predictions. We adopt a simple and standard noti…
Replicable Reinforcement Learning with Linear Function Approximation
Eric Eaton, Marcel Hussing, Michael Kearns +3
Replication of experimental results has been a challenge faced by many scientific disciplines, including the field of machine learning. Recent work on the theory of machine learnin…
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces
Eric Eaton, Marcel Hussing, Michael Kearns +3
In traditional reinforcement learning (RL), the learner aims to solve a single objective optimization problem: find the policy that maximizes expected reward. However, in many real…
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…