We define a general framework for a large class of combinatorial multi-armed bandit (CMAB) problems, where simple arms with unknown distributions form super arms. In each round, a super arm is played and the outcomes of its related simple arms are observed, which helps t...
We present a meta-algorithm that combines such classifier with a bandit algorithm in a feedback loop. Contextual bandits with similarity information Alex Slivkins (COLT 2011) Abstract Interpreting the current time as a part of the contextual information, we obtain a very general bandit framework ...
Embodiments herein provide a method and an apparatus for performing Outer Link Loop Adaptation (OLLA) as a Multi-Armed Bandit (MAB). The method includes associating each of the predefined Modulation and Coding Scheme (MCS) values to each of the arms of the MAB. The method includes determining...
内容提示: Multi-armed Bandit Problems with Dependent ArmsSandeep Pandey spandey@yahoo-inc.comDeepayan Chakrabarti deepay@yahoo-inc.comDeepak Agarwal dagarwal@yahoo-inc.comYahoo! Research, Sunnyvale, CAAbstractWe provide a framework to exploit dependen-cies among arms in multi-armed bandit prob-lems...
We define a general framework for a large class of combinatorial multi-armed bandit (CMAB) problems, where simple arms with unknown istributions form . In each round, a super arm is played and the outcomes of its related simple arms are observed, which helps the selection of super arms in...
bandit¶ ↑ Bandit is a multi-armed bandit optimization framework for Rails. It provides an alternative to A/B testing in Rails. For background and a comparison with A/B testing, see the whybandit.rdoc document or the blog post here. Installation¶ ↑ First, add the following to your...
Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject. ...
To provide a framework where one could model scenarios like the one sketched above, we present the adversarial bandit problem, a variant of the bandit problem in which no statistical assumptions are made about the generation of rewards. We only assume that each slot machine is initially assigned...
We introduce a novel variant of the multi-armed bandit problem, in which bandits are streamed one at a time to the player, and at each point, the player can either choose to pull the current bandit or move on to the next bandit. Once a player has moved on from a bandit, they may...
The multi-armed bandit problem is both deeply theoretical and deeply practical. More often than not, real-world scenarios are complex, encompassing many aspects and factors. If we try to solve everything right away, then we probably won’t solve anything at all. Theory allows us to divide an...