mlsanityData to 14 Oct 2026

Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learning

arXiv 2410.23393 ↗First posted 30 Oct 2024cs.LG cs.AI cs.MAcited by 1

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

Abstract (arXiv)

We introduce a framework that integrates variational autoencoders (VAE) with reinforcement learning (RL) to balance system performance and resource usage in multi-agent systems by dynamically adjusting network structures over time. A key innovation of this method is its capability to handle the vast action space of the network structure. This is achieved by combining Variational Auto-Encoder and Deep Reinforcement Learning to control the latent space encoded from the network structures. The proposed method, evaluated on the modified OpenAI particle environment under various scenarios, not only demonstrates superior performance compared to baselines but also reveals interesting strategies and insights through the learned behaviors.

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 26 of its 73 references that are in the map. First posted on arXiv 30 Oct 2024.

Builds on

26 of 73 references.

References · 26

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14 more
  1. 2019A Survey of Deep Reinforcement Learning in Video Gamescited by 73
  2. 2021Learning to design drug-like molecules in three-dimensional space using deep generative modelscited by 37
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networkscited by 0

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  5. 2026SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control
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