Safety Verification of Non-Deterministic Policies in Reinforcement Learning
Reinforcement Learning represents a powerful paradigm in artificial intelligence, enabling agents to learn optimal behaviors through interactions with their environment. However, ensuring the safety of policies learned in non-deterministic environments, where outcomes are inherently uncertain and va...
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| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
IEEE
2024-01-01
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| coleção: | IEEE Access |
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| Acesso em linha: | https://ieeexplore.ieee.org/document/10786219/ |
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