Playing with and against Hedge
Machine Learning
2018-12-10 v1 Multiagent Systems
Networking and Internet Architecture
Performance
Machine Learning
Abstract
Hedge has been proposed as an adaptive scheme, which guides an agent's decision in resource selection and distribution problems that can be modeled as a multi-armed bandit full information game. Such problems are encountered in the areas of computer and communication networks, e.g. network path selection, load distribution, network interdiction, and also in problems in the area of transportation. We study Hedge under the assumption that the total loss that can be suffered by the player in each round is upper bounded. In this paper, we study the worst performance of Hedge.
Keywords
Cite
@article{arxiv.1812.03131,
title = {Playing with and against Hedge},
author = {Miltiades E. Anagnostou and Maria A. Lambrou},
journal= {arXiv preprint arXiv:1812.03131},
year = {2018}
}