5 papers · 1 filter
Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers
Sikata Sengupta, Guangyi Liu, Omer Gottesman +4
Optimizing the consolidation process in container-based fulfillment centers requires trading off competing objectives such as processing speed, resource usage, and space utilizatio…
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…
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…