Counterexample-Guided Repair of Reinforcement Learning Systems Using Safety Critics

David Boetius; Stefan Leue

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arXiv · 2405.15430

Counterexample-Guided Repair of Reinforcement Learning Systems Using Safety Critics

David Boetius·Stefan Leue

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Abstract

Naively trained Deep Reinforcement Learning agents may fail to satisfy vital safety constraints. To avoid costly retraining, we may desire to repair a previously trained reinforcement learning agent to obviate unsafe behaviour. We devise a counterexample-guided repair algorithm for repairing reinforcement learning systems leveraging safety critics. The algorithm jointly repairs a reinforcement learning agent and a safety critic using gradient-based constrained optimisation.

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David Boetius, Stefan Leue. 2024-05-24. Counterexample-Guided Repair of Reinforcement Learning Systems Using Safety Critics. https://arxiv.org/abs/2405.15430

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