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Bandits with Feedback Graphs and Switching Costs
Raman Arora · Teodor Vanislavov Marinov · Mehryar Mohri

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #48
We study the adversarial multi-armed bandit problem where the learner is supplied with partial observations modeled by a \emph{feedback graph} and where shifting to a new action incurs a fixed \emph{switching cost}. We give two new algorithms for this problem in the informed setting. Our best algorithm achieves a pseudo-regret of $\tilde O(\gamma(G)^{\frac{1}{3}}T^{\frac{2}{3}})$, where $\gamma(G)$ is the domination number of the feedback graph. This significantly improves upon the previous best result for the same problem, which was based on the independence number of $G$. We also present matching lower bounds for our result that we describe in detail. Finally, we give a new algorithm with improved policy regret bounds when partial counterfactual feedback is available.

Author Information

Raman Arora (Johns Hopkins University)
Teodor Vanislavov Marinov (Johns Hopkins University)
Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research)

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