Safe Online Learning via Smooth Safety-Structured Policy Composition
Safe online reinforcement learning requires policies to respect safety constraints while maintaining smooth optimization dynamics. Existing approaches typically rely on either strict safety enforcement via action interventions, which introduce discontinuities in system interaction and learning, or soft safety constraint formulations, which preserve smooth learning but provide limited safety assurance. We propose AutoSafe, a safety-aware policy architecture that integrates structured safety monitoring and intervention directly into the action generation process. This design enables smooth, risk-dependent transitions between performance-driven and safety-preserving behaviors, resulting in c...
| Authors | Hongpeng Cao, Liqun Zhao, Yuliang Gu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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