An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits
Abstract
We present a new strategy for gap estimation in randomized algorithms for multiarmed bandits and combine it with the EXP3++ algorithm of Seldin and Slivkins (2014). In the stochastic regime the strategy reduces dependence of regret on a time horizon from $(\ln t)^3$ to $(\ln t)^2$ and eliminates an additive factor of order $∆e^1/∆^2$, where $∆$ is the minimal gap of a problem instance. In the adversarial regime regret guarantee remains unchanged.
Cite
Text
Seldin and Lugosi. "An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits." Proceedings of the 2017 Conference on Learning Theory, 2017.Markdown
[Seldin and Lugosi. "An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits." Proceedings of the 2017 Conference on Learning Theory, 2017.](https://mlanthology.org/colt/2017/seldin2017colt-improved/)BibTeX
@inproceedings{seldin2017colt-improved,
title = {{An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits}},
author = {Seldin, Yevgeny and Lugosi, Gábor},
booktitle = {Proceedings of the 2017 Conference on Learning Theory},
year = {2017},
pages = {1743-1759},
volume = {65},
url = {https://mlanthology.org/colt/2017/seldin2017colt-improved/}
}