Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning
A new paper explores using quasi-Monte Carlo (QMC) weight initialization for meta-reinforcement learning. The method shows faster convergence on similar unseen tasks compared to orthogonal initialization, but orthogonal remains superior on dissimilar tasks.
-->
[Submitted on 21 Jul 2026]
Title:Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning
View a PDF of the paper titled Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning, by Julian G. Soltes
View PDF HTML (experimental)
Abstract:This paper explores the efficacy of quasi-Monte Carlo (QMC) weight initialization for meta-reinforcement learning within modern benchmark environments. Various sampling methods are used to bound a population-based search and aggregate an optimal prior from a baseline set of tasks. The QMC meta-priors show improvements in training convergence compared to modern orthogonal (SB3) defaults when extrapolated to similar unseen continuous control environments. In dissimilar tasks, the orthogonal orientation was globally superior for an unbiased search.
Comments: 6 pages, 6 figures, 1 table
Subjects:
Machine Learning (cs.LG); Optimization and Control (math.OC)
MSC classes: 90C59
ACM classes: I.2.6; G.1.6
Cite as: arXiv:2607.21637 [cs.LG]
(or arXiv:2607.21637v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.21637
arXiv-issued DOI via DataCite
Submission history
From: Julian Soltes [view email] [v1] Tue, 21 Jul 2026 03:30:58 UTC (3,700 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning, by Julian G. Soltes
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.LG
new | recent | 2026-07
Change to browse by:
cs math math.OC
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)