Offline Model-Based Adaptable Policy Learning for Decision-Making in Out-of-Support Regions
In reinforcement learning, a promising direction to avoid online trial-and-error costs is learning from an offline dataset. Current offline reinforcement learning methods commonly learn in the policy space constrained to in-support regions by the offline dataset, in order to ensure the robustness of...
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Détails bibliographiques
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 12 vom: 01. Dez., Seite 15260-15274
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| Auteur principal: |
Chen, Xiong-Hui
(Auteur) |
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| Autres auteurs: |
Luo, Fan-Ming,
Yu, Yang,
Li, Qingyang,
Qin, Zhiwei,
Shang, Wenjie,
Ye, Jieping |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2023
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence
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| Sujets: | Journal Article |
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