A probabilistic interpretation of self-paced learning with applications to reinforcement learning

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021-07-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
Journal of Machine Learning Research, Volume 22
Abstract
Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives. For reinforcement learning (RL), curricula are especially interesting, as the underlying optimization has a strong tendency to get stuck in local optima due to the exploration-exploitation trade-off. Recently, a number of approaches for an automatic generation of curricula for RL have been shown to increase performance while requiring less expert knowledge compared to manually designed curricula. However, these approaches are seldomly investigated from a theoretical perspective, preventing a deeper understanding of their mechanics. In this paper, we present an approach for automated curriculum generation in RL with a clear theoretical underpinning. More precisely, we formalize the well-known self-paced learning paradigm as inducing a distribution over training tasks, which trades off between task complexity and the objective to match a desired task distribution. Experiments show that training on this induced distribution helps to avoid poor local optima across RL algorithms in different tasks with uninformative rewards and challenging exploration requirements.
Description
Funding Information: This project has received funding from the DFG project PA3179/1-1 (ROBOLEAP) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 640554 (SKILLS4ROBOTS). Calculations for this research were conducted on the Lichtenberg high performance computer of the TU Darmstadt. Publisher Copyright: © 2021 Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen.
Keywords
Curriculum learning, Reinforcement learning, Rl-as-inference, Self-paced learning, Tempered inference
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Citation
Klink, P, Abdulsamad, H, Belousov, B, D'Eramo, C, Peters, J & Pajarinen, J 2021, ' A probabilistic interpretation of self-paced learning with applications to reinforcement learning ', Journal of Machine Learning Research, vol. 22 . < https://www.jmlr.org/papers/volume22/21-0112/21-0112.pdf >