基于高斯过程动力学模型的恢复式 Shielding 安全强化学习方法
Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models
AI 导读
一项发表于 AAMAS 2026 的研究提出基于恢复机制的 shielding 安全强化学习框架，可为未知非线性连续动力学系统提供可证明的安全下界。
正文 · 原文
Abstract:Reinforcement learning (RL) is a powerful framework for optimal decision-making and control but often lacks provable guarantees for safety-critical applications. In this paper, we introduce a novel recovery-based shielding framework that enables safe RL with a provable safety lower bound for unknown and non-linear continuous dynamical systems. The proposed approach integrates a backup policy (shield) with the RL agent, leveraging Gaussian process (GP) based uncertainty quantification to predict potential violations of safety constraints, dynamically recovering to safe trajectories only when necessary. Experience gathered by the 'shielded' agent is used to construct the GP models, with policy optimization via internal model-based sampling - enabling unrestricted exploration and sample efficient learning, without compromising safety. Empirically our approach demonstrates strong performance and strict safety-compliance on a suite of continuous control environments.
| Comments: | AAMAS 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2602.12444 [cs.LG] |
| (or arXiv:2602.12444v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.12444 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.65109/EAHU8566
DOI(s) linking to related resources |
Submission history
From: Alexander W. Goodall [view email]
[v1]
Thu, 12 Feb 2026 22:03:35 UTC (548 KB)
[v2]
Tue, 17 Feb 2026 09:48:52 UTC (543 KB)
[v3]
Fri, 30 Oct 2026 10:42:27 UTC (543 KB)
来源：arXiv cs.AI · arxiv.org