Self Paced Gaussian Contextual Reinforcement Learning
Curriculum learning improves reinforcement learning (RL) efficiency by sequencing tasks from simple to complex. However, many self-paced curriculum methods rely on computationally expensive inner-loop optimizations, limiting their scalability in high-dimensional context spaces. In this paper, we propose Self-Paced Gaussian Curriculum Learning (SPGL), a novel approach that avoids costly numerical procedures by leveraging a closed-form update rule for Gaussian context distributions. SPGL maintains the sample efficiency and adaptability of traditional self-paced methods while substantially reducing computational overhead. We provide theoretical guarantees on convergence and validate our meth...
| Authors | Mohsen Sahraei Ardakani, Rui Song |
|---|---|
| Primary category | cs.LG |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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