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Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits
Abstract
The Contextual Multi-Armed Bandits (CMAB) framework is pivotal for learning to make decisions. However, due to challenges in deploying online algorithms, there is a shift towards offline policy learning, which relies on pre-existing datasets. This study examines the relationship between the quality of these datasets and the performance of offline policy learning algorithms, specifically, Neural Greedy and NeuraLCB. Our results demonstrate that NeuraLCB can learn from various datasets, while Neural Greedy necessitates extensive coverage of the action-space for effective learning. Moreover, the way data is collected significantly affects offline methods’ efficiency. This underscores the critical role of dataset quality in offline policy learning.
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.5220/0012311000003636
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Januszewski P., Grzegorzek D., Czarnul P.: Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits// / : , 2024,
- DOI:
- Digital Object Identifier (open in new tab) 10.5220/0012311000003636
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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