3 papers
cs.LG2026
VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning
Joery A. de Vries, Jinke He, Yaniv Oren +3
Optimally trading-off exploration and exploitation is the holy grail of reinforcement learning as it promises maximal data-efficiency for solving any task. Bayes-optimal agents ach…
cs.LG2025
Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks
Joery A. de Vries, Jinke He, Mathijs M. de Weerdt +1
Meta-reinforcement learning trains a single reinforcement learning agent on a distribution of tasks to quickly generalize to new tasks outside of the training set at test time. Fro…
cs.LG2025
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
Jacobus G. M. van der Linden, Daniël Vos, Daniël Vos +4
Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally opti…