Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| author | Bethell, Daniel Gerasimou, Simos Calinescu, Radu Imrie, Calum |
| author_facet | Bethell, Daniel Gerasimou, Simos Calinescu, Radu Imrie, Calum |
| contents | Empowering safe exploration of reinforcement learning (RL) agents during training is a critical challenge towards their deployment in many real-world scenarios. When prior knowledge of the domain or task is unavailable, training RL agents in unknown, black-box environments presents an even greater safety risk. 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, and uses this knowledge to protect the RL agent from executing actions that yield likely hazardous outcomes. Our comprehensive experimental evaluation against state-of-the-art safe RL exploration techniques shows that ADVICE significantly reduces safety violations (approx 50%) during training, with a competitive outcome reward compared to other techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18180 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding Bethell, Daniel Gerasimou, Simos Calinescu, Radu Imrie, Calum Artificial Intelligence Machine Learning Empowering safe exploration of reinforcement learning (RL) agents during training is a critical challenge towards their deployment in many real-world scenarios. When prior knowledge of the domain or task is unavailable, training RL agents in unknown, black-box environments presents an even greater safety risk. 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, and uses this knowledge to protect the RL agent from executing actions that yield likely hazardous outcomes. Our comprehensive experimental evaluation against state-of-the-art safe RL exploration techniques shows that ADVICE significantly reduces safety violations (approx 50%) during training, with a competitive outcome reward compared to other techniques. |
| title | Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.18180 |