Computer Science > Machine Learning
arXiv:2401.06604 (cs)
[Submitted on 12 Jan 2024 (v1), last revised 18 Mar 2024 (this version, v3)]
Title:Identifying Policy Gradient Subspaces
Authors:Jan Schneider,Pierre Schumacher,Simon Guist,Le Chen,Daniel Häufle,Bernhard Schölkopf,Dieter Büchler
View a PDF of the paper titled Identifying Policy Gradient Subspaces, by Jan Schneider and 6 other authors
View PDFHTML (experimental)Abstract:Policy gradient methods hold great potential for solving complex continuous control tasks. Still, their training efficiency can be improved by exploiting structure within the optimization problem. Recent work indicates that supervised learning can be accelerated by leveraging the fact that gradients lie in a low-dimensional and slowly-changing subspace. In this paper, we conduct a thorough evaluation of this phenomenon for two popular deep policy gradient methods on various simulated benchmark tasks. Our results demonstrate the existence of such gradient subspaces despite the continuously changing data distribution inherent to reinforcement learning. These findings reveal promising directions for future work on more efficient reinforcement learning, e.g., through improving parameter-space exploration or enabling second-order optimization.
| Comments: | Published as conference paper at ICLR 2024 |
| Subjects: | Machine Learning (cs.LG) |
| ACM classes: | I.2.6 |
| Cite as: | arXiv:2401.06604 [cs.LG] |
| (orarXiv:2401.06604v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2401.06604 arXiv-issued DOI via DataCite |
Submission history
From: Jan Schneider [view email][v1] Fri, 12 Jan 2024 14:40:55 UTC (2,030 KB)
[v2] Mon, 15 Jan 2024 14:39:10 UTC (2,030 KB)
[v3] Mon, 18 Mar 2024 09:51:00 UTC (2,208 KB)
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View a PDF of the paper titled Identifying Policy Gradient Subspaces, by Jan Schneider and 6 other authors
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