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

arXiv: https://arxiv.org/abs/2410.23393 · HTML: https://mlsanity.com/paper/2410.23393/

## Key facts

- Cited by 1 papers 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.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 26 of 26)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [Learning Deep Generative Models of Graphs](https://mlsanity.com/paper/1803.03324/) (2018)
- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [A Systematic Survey on Deep Generative Models for Graph Generation](https://mlsanity.com/paper/2007.06686/) (2020)
- [Action Branching Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1711.08946/) (2017)
- [A Survey of Deep Reinforcement Learning in Video Games](https://mlsanity.com/paper/1912.10944/) (2019)
- [Learning to design drug-like molecules in three-dimensional space using deep generative models](https://mlsanity.com/paper/2104.08474/) (2021)
- [Managing engineering systems with large state and action spaces through deep reinforcement learning](https://mlsanity.com/paper/1811.02052/) (2018)
- [Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2002.05513/) (2020)
- [State of the Art Control of Atari Games Using Shallow Reinforcement Learning](https://mlsanity.com/paper/1512.01563/) (2015)
- [A survey on text generation using generative adversarial networks](https://mlsanity.com/paper/2212.11119/) (2022)
- [Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation](https://mlsanity.com/paper/2105.04184/) (2021)
- [Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport](https://mlsanity.com/paper/2103.15260/) (2021)
- [Integrated Design and Governance of Agentic AI Systems through Adaptive Information Modulation](https://mlsanity.com/paper/2409.10372/) (2024)
- [Efficient Network Structures with Separable Heterogeneous Connection Costs](https://mlsanity.com/paper/1504.06634/) (2015)
- [Distributed Resource Management in Systems of Systems: An Architecture Perspective](https://mlsanity.com/paper/1604.02114/) (2016)
- [Network Interventions: Applying Network Science for Pragmatic Action in Public Administration and Policy](https://mlsanity.com/paper/2109.08197/) (2021)
- [Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings](https://mlsanity.com/paper/2012.15702/) (2020)
- [Physics guided deep learning generative models for crystal materials discovery](https://mlsanity.com/paper/2112.03528/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks](https://mlsanity.com/paper/2607.18288/) (2026)

## 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.