Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies

2022/02/01 by Carlos Güemes-Palau, Paul Almasan, Güemes-Palau, Carlos +11 · 1 citation
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Software-Defined Networks and 5G

paper · pdf · doi:10.48550/arxiv.2202.00360

openalex publication_date 2022/02/01 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28

Abstract

The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is increasing the complexity of managing modern communication networks. As a result, the community proposed the Digital Twin Networks (DTN) as a key enabler of efficient network management. Network operators can leverage the DTN to perform different optimization tasks (e.g., Traffic Engineering, Network Planning). Deep Reinforcement Learning (DRL) showed a high performance when applied to solve network optimization problems. In the context of DTN, DRL can be leveraged to solve optimization problems without directly impacting the real-world network behavior. However, DRL scales poorly with the problem size and complexity. In this paper, we explore the use of Evolutionary Strategies (ES) to train DRL agents for solving a routing optimization problem. The experimental results show that ES achieved a training time speed-up of 128 and 6 for the NSFNET and GEANT2 topologies respectively.

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