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.

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