Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning

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.

Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2607.21637 [cs.LG]

(or arXiv:2607.21637v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.21637 Focus to learn more arXiv-issued DOI via DataCite

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