Conditional Sequence Modeling for Safe Reinforcement Learning
Offline safe reinforcement learning (RL) aims to learn policies from a fixed dataset while maximizing performance under cumulative cost constraints. In practice, deployment requirements often vary across scenarios, necessitating a single policy capable of zero-shot adaptation to different cost thres...
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| Główni autorzy: | , , , , , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
MDPI AG
2026-03-01
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| Seria: | Mathematics |
| Hasła przedmiotowe: | |
| Dostęp online: | https://www.mdpi.com/2227-7390/14/6/1015 |
| Etykiety: |
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