🤖 AI Summary
This work addresses the limited generalization and low adaptation efficiency of initial policy priors in meta-reinforcement learning by introducing quasi-Monte Carlo (QMC) sampling into the weight initialization phase of meta-learning for the first time. By integrating bounded population search with top-prior aggregation, the proposed approach constructs an effective initialization strategy for continuous control tasks. Experimental results demonstrate that QMC-based initialization significantly accelerates training convergence in unseen environments with high task similarity, whereas conventional orthogonal initialization retains an advantage when tasks are substantially dissimilar. This study reveals the critical influence of task similarity on the effectiveness of initialization strategies and offers a novel perspective for enabling efficient warm-starting in 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.