mlsanityData to 14 Oct 2026

Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management

arXiv 2506.13153 ↗First posted 16 Jun 2025cs.NI cs.LGcited by 1

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

An internet network service provider manages its network with multiple objectives, such as high quality of service (QoS) and minimum computing resource usage. To achieve these objectives, a reinforcement learning-based (RL) algorithm has been proposed to train its network management agent. Usually, their algorithms optimize their agents with respect to a single static reward formulation consisting of multiple objectives with fixed importance factors, which we call preferences. However, in practice, the preference could vary according to network status, external concerns and so on. For example, when a server shuts down and it can cause other servers' traffic overloads leading to additional shutdowns, it is plausible to reduce the preference of QoS while increasing the preference of minimum computing resource usages. In this paper, we propose new RL-based network management agents that can select actions based on both states and preferences. With our proposed approach, we expect a single agent to generalize on various states and preferences. Furthermore, we propose a numerical method that can estimate the distribution of preference that is advantageous for unbiased training. Our experiment results show that the RL agents trained based on our proposed approach significantly generalize better with various preferences than the previous RL approaches, which assume static preference during training. Moreover, we demonstrate several analyses that show the advantages of our numerical estimation method.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 16 references that are in the map. First posted on arXiv 16 Jun 2025.

Builds on

9 of 16 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  3. 2015Gated Graph Sequence Neural Networkscited by 796
  4. 2019A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptationcited by 177
  5. 2015Network Function Virtualization: State-of-the-art and Research Challengescited by 9
  6. 2020Graph Neural Network based Service Function Chaining for Automatic Network Controlcited by 7
  7. 2015On Orchestrating Virtual Network Functions in NFVcited by 3
  8. 2020Reinforcement Learning of Graph Neural Networks for Service Function Chainingcited by 3
  9. 2021Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chainscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packingcited by 0

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  4. 2023Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL
  5. 2026Multi-Agent Reinforcement Learning via Agent-Specific Preference
  6. 2026GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning
  7. 2025Preference-based Multi-Objective Reinforcement Learning
  8. 2022A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario