Computer Science > Machine Learning
[Submitted on 27 Jul 2023]
Title:Approximate Model-Based Shielding for Safe Reinforcement Learning
View PDFAbstract:Reinforcement learning (RL) has shown great potential for solving complex tasks in a variety of domains. However, applying RL to safety-critical systems in the real-world is not easy as many algorithms are sample-inefficient and maximising the standard RL objective comes with no guarantees on worst-case performance. In this paper we propose approximate model-based shielding (AMBS), a principled look-ahead shielding algorithm for verifying the performance of learned RL policies w.r.t. a set of given safety constraints. Our algorithm differs from other shielding approaches in that it does not require prior knowledge of the safety-relevant dynamics of the system. We provide a strong theoretical justification for AMBS and demonstrate superior performance to other safety-aware approaches on a set of Atari games with state-dependent safety-labels.
Submission history
From: Alexander W. Goodall [view email][v1] Thu, 27 Jul 2023 15:19:45 UTC (5,723 KB)
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