Network Slicing Via Transfer Learning Aided Distributed Deep Reinforcement Learning

Abstract

Deep reinforcement learning (DRL) has been increasingly employed to handle the dynamic and complex resource management in network slicing. The deployment of DRL policies in real networks, however, is complicated by heterogeneous cell conditions. In this paper, we propose a novel transfer learning (TL) aided multi-agent deep reinforcement learning (MADRL) approach with inter-agent similarity analysis for inter-cell inter-slice resource partitioning. First, we design a coordinated MADRL method with information sharing to intelligently partition resource to slices and manage inter-cell interference. Second, we propose an integrated TL method to transfer the learned DRL policies among different local agents for accelerating the policy deployment. The method is composed of a new domain and task similarity measurement approach and a new knowledge transfer approach, which resolves the problem of from whom to transfer and how to transfer. We evaluated the proposed solution with extensive simul

Stats

• citations9
• S2 citations—
• github stars0
• HF likes0
• heat score7.50
• arxiv keyhu2023network

Related papers

• Safe And Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach (2023)11.29
• Resource Management In Wireless Networks Via Multi-agent Deep Reinforcement Learning (2020)16.43
• Prioritizing Latency With Profit: A Drl-based Admission Control For 5G Network Slices (2025)0.00
• Decentralized Task Scheduling In Distributed Systems: A Deep Reinforcement Learning Approach (2026)0.00
• Dynamics Of Resource Allocation In O-rans: An In-depth Exploration Of On-policy And Off-policy Deep Reinforcement Learning For Real-time Applications (2024)2.26
• Meta-reinforcement Learning For Fast And Data-efficient Spectrum Allocation In Dynamic Wireless Networks (2025)0.00
• Deep Reinforcement Learning For Distributed And Uncoordinated Cognitive Radios Resource Allocation (2022)0.00
• Deep Reinforcement Learning For Distributed Uncoordinated Cognitive Radios Resource Allocation (2019)0.00