2 papers
cs.LG2026
Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification
Anders Jonsson, Emilie Kaufmann, Gianmarco Tedeschi +1
We present HBPI-UCRL, a model-based algorithm for hierarchical reinforcement learning (HRL) that learns high-level and low-level policies in parallel. HBPI-UCRL exploits the fact t…
cs.LG2026
Sampling-guided exploration of active feature selection policies
Gabriel Bernardino, Anders Jonsson, Patrick Clarysse +1
Determining the most appropriate features for machine learning predictive models is challenging regarding performance and feature acquisition costs. In particular, global feature c…