SPAARS: Safer RL Policy Alignment through Abstract Exploration and Refined Exploitation of Action Space
Offline-to-online reinforcement learning (RL) offers a promising paradigm for robotics by pre-training policies on safe, offline demonstrations and fine-tuning them via online interaction. However, a fundamental challenge remains: how to safely explore online without deviating from the behavioral support of the offline data? While recent methods leverage conditional variational autoencoders (CVAEs) to bound exploration within a latent space, they inherently suffer from an exploitation gap -- a performance ceiling imposed by the decoder's reconstruction loss. We introduce SPAARS, a curriculum learning framework that initially constrains exploration to the low-dimensional latent manifold fo...
| Authors | Swaminathan S K, Aritra Hazra |
|---|---|
| Primary category | cs.LG |
| Published | 10 March 2026 |
| Last updated at source | 11 March 2026 |
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