Multi-Agent Deep Reinforcement Learning in Cognitive Inter-Domain Networking with Multi-Broker Orchestration

Xiaoliang Chen; Baojia Li; Roberto Proietti; Zuqing Zhu; S. J. Ben Yoo

doi:10.1364/ofc.2019.m2a.2

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JOURNAL ARTICLE

Multi-Agent Deep Reinforcement Learning in Cognitive Inter-Domain Networking with Multi-Broker Orchestration

Xiaoliang Chen Baojia Li Roberto Proietti Zuqing Zhu S. J. Ben Yoo

Year: 2019 Pages: M2A.2-M2A.2

DOI: 10.1364/ofc.2019.m2a.2

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Abstract

This paper proposes, for the first time, a cognitive inter-domain networking framework with multi-broker orchestration and multi-agent deep reinforcement learning for multi-domain optical networks. Simulation results show > 17% blocking reduction compared to the baselines.

Keywords:

Orchestration Reinforcement learning Computer science Domain (mathematical analysis) Cognition Distributed computing Human–computer interaction Artificial intelligence Computer network Psychology Neuroscience

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Topics

Neural Networks and Reservoir Computing

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Optical Network Technologies

Physical Sciences → Engineering → Electrical and Electronic Engineering

Optical Network Technologies

Physical Sciences → Engineering → Electrical and Electronic Engineering

Multi-Agent Deep Reinforcement Learning in Cognitive Inter-Domain Networking with Multi-Broker Orchestration

Abstract

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Citation History

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