van Hoof, Herke

Addressing Function Approximation Error in Actor-Critic Methods

Fujimoto, S., van Hoof, H., & Meger, D. (2018) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 3,650

Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems

Huang, J., Oosterhuis, H., de Rijke, M., & van Hoof, H. (2020) · Fourteenth ACM Conference on Recommender Systems

Recommender Systems and TechniquesAdvanced Bandit Algorithms Research

Cited by 64

A Survey of Exploration Methods in Reinforcement Learning

Amin, S., Gomrokchi, M., Satija, H., van Hoof, H., & Precup, D. (2021) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 41

Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems

Huang, J., Oosterhuis, H., Mansoury, M., van Hoof, H., & de Rijke, M. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 17

Multi-Agent MDP Homomorphic Networks

van der Pol, E., van Hoof, H., Oliehoek, F. A., & Welling, M. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 5