A Predictive Q-Learning Algorithm for Deflection Routing in Buffer-Less Networks

被引：8

作者：

Haeri, Soroush ^{[1
]}

Arianezhad, Majid ^{[1
]}

Trajkovic, Ljiljana ^{[1
]}

机构：

[1] Simon Fraser Univ, Vancouver, BC, Canada

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013) | 2013年

关键词：

Computer networks; buffer-less networks; deflection routing; reinforcement learning; predictive Q-learning; PERFORMANCE EVALUATION; REINFORCEMENT;

D O I：

10.1109/SMC.2013.135

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we introduce a predictive Q-learning deflection routing (PQDR) algorithm for buffer-less networks. Q-learning, one of the reinforcement learning (RL) algorithms, has been considered for routing in computer networks. The RL-based algorithms have not been widely deployed in computer networks where their inherent random nature is undesired. However, their randomness is sought-after in certain cases such as deflection routing, which may be employed to ameliorate packet loss caused by contention in buffer-less networks. We compare the proposed algorithm with two existing reinforcement learning-based deflection routing algorithms. Simulation results show that the proposed algorithm decreases the burst loss probability in the case of heavy traffic load while it requires fewer deflections. The PQDR algorithm is implemented using the ns-3 network simulator.

引用

页码：764 / 769

页数：6

共 17 条

[1]

[Anonymous], 2013, Ns-3 network simulator

[2]

[Anonymous], 2020, Reinforcement Learning, An Introduction

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