Approximate Model-Based Shielding for Safe Reinforcement Learning

• URL: http://arxiv.org/abs/2308.00707v1
• Date: Thu, 27 Jul 2023 15:19:45 GMT
• Title: Approximate Model-Based Shielding for Safe Reinforcement Learning
• Authors: Alexander W. Goodall, Francesco Belardinelli
• Abstract summary: We propose a principled look-ahead shielding algorithm for verifying the performance of learned RL policies. Our algorithm differs from other shielding approaches in that it does not require prior knowledge of the safety-relevant dynamics of the system. We demonstrate superior performance to other safety-aware approaches on a set of Atari games with state-dependent safety-labels.
• Score: 83.55437924143615
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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