Multiagent Deep Reinforcement Learning for Cost- and Delay-Sensitive Virtual Network Function Placement and Routing-Reference-Cited by-同舟云学术

Multiagent Deep Reinforcement Learning for Cost- and Delay-Sensitive Virtual Network Function Placement and Routing

Author:

Wang Shaoyang¹^ORCID,Yuen Chau²^ORCID,Ni Wei³^ORCID,Guan Yong Liang⁴^ORCID,Lv Tiejun¹^ORCID

Affiliation:

1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China

2. Engineering Product Development, SUTD, Somapah Rd, Singapore

3. Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, NSW, Australia

4. School of Electrical and Electronic Engineering, NTU, Nanyang Ave, Singapore

Funder

China Scholarship Council, and part of the work was done during the visit to SUTD in 2021

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

Reference46 articles.

1. Deterministic policy gradient algorithms;silver;Proc Int Conf Mach Learn (ICML),2014

2. Continuous control with deep reinforcement learning;lillicrap;arXiv 1509 02971,2015

4. Neural combinatorial optimization with reinforcement learning;bello;arXiv 1611 09940,2016

5. Multi-agent actor-critic for mixed cooperative-competitive environments;lowe;arXiv 1706 02275,2017

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