On the Minimax Regret for Linear Bandits in a wide variety of Action Spaces
Banerjee, Debangshu, Gopalan, Aditya
–arXiv.org Artificial Intelligence
As noted in the works of \cite{lattimore2020bandit}, it has been mentioned that it is an open problem to characterize the minimax regret of linear bandits in a wide variety of action spaces. In this article we present an optimal regret lower bound for a wide class of convex action spaces.
arXiv.org Artificial Intelligence
Jan-9-2023
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- Europe > United Kingdom
- England > Cambridgeshire > Cambridge (0.05)
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- Research Report (0.40)
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