186 citations · 210 across the 15 of their papers we have counts for
16 papers
Correcting Within-Group Self-Selection Bias in Prioritized Replay
Oscar Miró López-Feliu, Herke van Hoof
Prioritized experience replay (PER) improves sample efficiency by replaying high-priority transitions, usually according to absolute temporal-difference error. In stochastic enviro…
A Goal-Set Characterization of Task Composition in the Boolean Task Algebra
Eduardo Terrés-Caballero, Herke van Hoof
The Boolean Task Algebra (BTA) provides a principled framework for zero-shot task composition in reinforcement learning by equipping goal-reaching tasks with Boolean operations. We…
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits
Masoud Mansoury, Bamshad Mobasher, Herke van Hoof
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This bias becomes particularly probl…
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
Guillermo Infante, David Kuric, Anders Jonsson +2
Conventional reinforcement learning (RL) methods can successfully solve a wide range of sequential decision problems. However, learning policies that can generalize predictably acr…
Hierarchical Reinforcement Learning for Power Network Topology Control
Blazej Manczak, Jan Viebahn, Herke van Hoof
Learning in high-dimensional action spaces is a key challenge in applying reinforcement learning (RL) to real-world systems. In this paper, we study the possibility of controlling…
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes
Tim Bakker, Herke van Hoof, Max Welling
Pool-based active learning (AL) is a promising technology for increasing data-efficiency of machine learning models. However, surveys show that performance of recent AL methods is…
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
Robert Loftin, Mustafa Mert Çelikok, Herke van Hoof +2
In multi-agent problems requiring a high degree of cooperation, success often depends on the ability of the agents to adapt to each other's behavior. A natural solution concept in…
Reusable Options through Gradient-based Meta Learning
David Kuric, Herke van Hoof
Hierarchical methods in reinforcement learning have the potential to reduce the amount of decisions that the agent needs to perform when learning new tasks. However, finding reusab…
Exposure-Aware Recommendation using Contextual Bandits
Masoud Mansoury, Bamshad Mobasher, Herke van Hoof
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when…
Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods
Niklas Höpner, Ilaria Tiddi, Herke van Hoof
Enabling reinforcement learning (RL) agents to leverage a knowledge base while learning from experience promises to advance RL in knowledge intensive domains. However, it has prove…
Hierarchies of Planning and Reinforcement Learning for Robot Navigation
Jan Wöhlke, Felix Schmitt, Herke van Hoof
Solving robotic navigation tasks via reinforcement learning (RL) is challenging due to their sparse reward and long decision horizon nature. However, in many navigation tasks, high…
Deep Policy Dynamic Programming for Vehicle Routing Problems
Wouter Kool, Herke van Hoof, Joaquim Gromicho +1
Routing problems are a class of combinatorial problems with many practical applications. Recently, end-to-end deep learning methods have been proposed to learn approximate solution…
Social Navigation with Human Empowerment driven Deep Reinforcement Learning
Tessa van der Heiden, Florian Mirus, Herke van Hoof
Mobile robot navigation has seen extensive research in the last decades. The aspect of collaboration with robots and humans sharing workspaces will become increasingly important in…
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera +2
Diversity of environments is a key challenge that causes learned robotic controllers to fail due to the discrepancies between the training and evaluation conditions. Training from…
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
Wouter Kool, Herke van Hoof, Max Welling
The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample elements without replacement. We show how to implicitly apply this 'Gumbe…
Attention, Learn to Solve Routing Problems!
Wouter Kool, Herke van Hoof, Max Welling
The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development. However, to push this idea towards practical…