Counterexample-Guided Repair of Reinforcement Learning Systems Using Safety Critics
<|reference_start|>Counterexample-Guided Repair of Reinforcement Learning Systems Using Safety Critics: 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.<|reference_end|>
@article{boetius2024counterexample-guided,
    title={Counterexample-Guided Repair of Reinforcement Learning Systems Using
  Safety Critics},
    author={David Boetius and Stefan Leue},
    journal={arXiv preprint arXiv:2405.15430},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15430},
    primaryClass={cs.LG cs.LO}
}