Name
Abstract | ||
|---|---|---|
We study model selection in stochastic bandit problems. Our approach relies on a master algorithm that selects its actions among candidate base algorithms. While this problem is studied for specific classes of stochastic base algorithms, our objective is to provide a method that can work with more general classes of stochastic base algorithms. We propose a master algorithm inspired by CORRAL \cite{DBLP:conf/colt/AgarwalLNS17} and introduce a novel and generic smoothing transformation for stochastic bandit algorithms that permits us to obtain $O(\sqrt{T})$ regret guarantees for a wide class of base algorithms when working along with our master. We exhibit a lower bound showing that even when one of the base algorithms has $O(\log T)$ regret, in general it is impossible to get better than $\Omega(\sqrt{T})$ regret in model selection, even asymptotically. We apply our algorithm to choose among different values of $\epsilon$ for the $\epsilon$-greedy algorithm, and to choose between the $k$-armed UCB and linear UCB algorithms. Our empirical studies further confirm the effectiveness of our model-selection method. |
Year | Venue | DocType |
|---|---|---|
2020 | NIPS 2020 | Conference |
Volume | Citations | PageRank |
33 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aldo Pacchiano | 1 | 10 | 11.62 |
| My V. T. Phan | 2 | 8 | 1.83 |
| Yasin Abbasi-Yadkori | 3 | 273 | 23.80 |
| Anup Rao | 4 | 581 | 32.80 |
| Julian Zimmert | 5 | 2 | 2.80 |
| Tor Lattimore | 6 | 174 | 29.15 |
| Csaba Szepesvári | 7 | 1774 | 157.06 |