Approximate Model-Based Shielding for Safe Reinforcement Learning

2023-07-27 · Alexander W. Goodall, Francesco Belardinelli

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.

📄 PDF Abstract BibTeX arXiv:2308.00707

Code (1)

sacktock/ambs 공식 구현 jax

Tasks

Atari Gamesmodelreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Safe Reinforcement Learning

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