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Comparing Transfer Learning and Rollout for Policy Adaptation in a Changing Network Environment
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0002-6343-7416
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2024 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Dynamic resource allocation for network services is pivotal for achieving end-to-end management objectives. Previous research has demonstrated that Reinforcement Learning (RL) is a promising approach to resource allocation in networks, allowing to obtain near-optimal control policies for non-trivial system configurations. Current RL approaches however have the drawback that a change in the system or the management objective necessitates expensive retraining of the RL agent.To tackle this challenge, practical solutions including offline retraining, transfer learning, and model-based rollout have been proposed. In this work, we study these methods and present comparative results that shed light on their respective performance and benefits. Our study finds that rollout achieves faster adaptation than transfer learning, yet its effectiveness highly depends on the accuracy of the system model.

Place, publisher, year, edition, pages
2024.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-346584OAI: oai:DiVA.org:kth-346584DiVA, id: diva2:1858754
Conference
IEEE/IFIP Network Operations and Management Symposium 6–10 May 2024 Seoul, South Korea
Note

QC 20240522

Available from: 2024-05-18 Created: 2024-05-18 Last updated: 2024-06-10Bibliographically approved

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No full text in DiVA

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Shahabsamani, Forough ShahabStadler, Rolf

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