Web: http://arxiv.org/abs/2206.11396

June 24, 2022, 1:10 a.m. | Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht

cs.LG updates on arXiv.org arxiv.org

Learning control from pixels is difficult for reinforcement learning (RL)
agents because representation learning and policy learning are intertwined.
Previous approaches remedy this issue with auxiliary representation learning
tasks, but they either do not consider the temporal aspect of the problem or
only consider single-step transitions. Instead, we propose Hierarchical
$k$-Step Latent (HKSL), an auxiliary task that learns representations via a
hierarchy of forward models that operate at varying magnitudes of step skipping
while also learning to communicate between levels …

arxiv hierarchical learning lg models

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY