Reinforcement Learning for Adaptive Routing
机器学习
2017-05-25 v1 人工智能
网络与互联网体系结构
摘要
Reinforcement learning means learning a policy--a mapping of observations into actions--based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. We present an application of gradient ascent algorithm for reinforcement learning to a complex domain of packet routing in network communication and compare the performance of this algorithm to other routing methods on a benchmark problem.
引用
@article{arxiv.cs/0703138,
title = {Reinforcement Learning for Adaptive Routing},
author = {Leonid Peshkin and Virginia Savova},
journal= {arXiv preprint arXiv:cs/0703138},
year = {2017}
}