2 citations · 2 across the 3 of their papers we have counts for
4 papers
Breaking the Grid: Distance-Guided Reinforcement Learning in Large Discrete Action Spaces
Heiko Hoppe, Fabian Akkerman, Wouter van Heeswijk +1
Reinforcement Learning (RL) is increasingly applied to large-scale decision-making problems like logistics, scheduling, and recommender systems, but existing algorithms struggle wi…
Combinatorial Optimization Augmented Machine Learning
Maximilian Schiffer, Heiko Hoppe, Yue Su +2
Combinatorial optimization augmented machine learning (COAML) has recently emerged as a powerful paradigm for integrating predictive models with combinatorial decision-making. By e…
Structured Reinforcement Learning for Combinatorial Decision-Making
Heiko Hoppe, Léo Baty, Louis Bouvier +2
Reinforcement learning (RL) is increasingly applied to real-world problems involving complex and structured decisions, such as routing, scheduling, and assortment planning. These s…
Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
Heiko Hoppe, Tobias Enders, Quentin Cappart +1
We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests or rejects these with the aim of maximi…