mlsanityData to 14 Oct 2026

Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning

arXiv 2607.21637 ↗First posted 21 Jul 2026cs.LG math.OCcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 7 references that are in the map. First posted on arXiv 21 Jul 2026.

Builds on

2 of 7 references.

References · 2

  1. 2025The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditionscited by 9
  2. 2025Hyperellipsoid Density Sampling: Exploitative Sequences to Accelerate High-Dimensional Numerical Optimizationcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Prior-Aligned Meta-RL: Thompson Sampling with Learned Priors and Guarantees in Finite-Horizon MDPs
  2. 2024Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees
  3. 2025Harnessing the Power of Reinforcement Learning for Adaptive MCMC
  4. 2024A CMDP-within-online framework for Meta-Safe Reinforcement Learning
  5. 2024Reinforcement Learning for Adaptive MCMC
  6. 2024MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
  7. 2024Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator
  8. 2025Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks