# Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning

arXiv: https://arxiv.org/abs/2607.21637 · HTML: https://mlsanity.com/paper/2607.21637/

## 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.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 2 of 2)

- [The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions](https://mlsanity.com/paper/2506.13234/) (2025)
- [Hyperellipsoid Density Sampling: Exploitative Sequences to Accelerate High-Dimensional Numerical Optimization](https://mlsanity.com/paper/2511.07836/) (2025)

## 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.