Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
<|reference_start|>Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding: Empowering safe exploration of reinforcement learning (RL) agents during training is a critical impediment towards deploying RL agents in many real-world scenarios. Training RL agents in unknown, black-box environments poses an even greater safety risk when prior knowledge of the domain/task is unavailable. We introduce ADVICE (Adaptive Shielding with a Contrastive Autoencoder), a novel post-shielding technique that distinguishes safe and unsafe features of state-action pairs during training, thus protecting the RL agent from executing actions that yield potentially hazardous outcomes. Our comprehensive experimental evaluation against state-of-the-art safe RL exploration techniques demonstrates how ADVICE can significantly reduce safety violations during training while maintaining a competitive outcome reward.<|reference_end|>
@article{bethell2024safe,
    title={Safe Reinforcement Learning in Black-Box Environments via Adaptive
  Shielding},
    author={Daniel Bethell, Simos Gerasimou, Radu Calinescu, Calum Imrie},
    journal={arXiv preprint arXiv:2405.18180},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18180},
    primaryClass={cs.AI cs.LG}
}