Resource Governance In Networked Systems Via Integrated Variational Autoencoders And Reinforcement Learning

Abstract

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keychen2024resource

Related papers

• Variational Empowerment As Representation Learning For Goal-based Reinforcement Learning (2021)0.00
• Resource Management In Wireless Networks Via Multi-agent Deep Reinforcement Learning (2020)16.43
• Bandwidth-constrained Variational Message Encoding For Cooperative Multi-agent Reinforcement Learning (2025)0.00
• Interpretable Option Discovery Using Deep Q-learning And Variational Autoencoders (2022)0.00
• Towards Governing Agent's Efficacy: Action-conditional \(\beta\)-vae For Deep Transparent Reinforcement Learning (2018)0.00
• VIME: Variational Information Maximizing Exploration (2016)0.00
• VIREL: A Variational Inference Framework For Reinforcement Learning (2018)0.00
• NVIF: Neighboring Variational Information Flow For Large-scale Cooperative Multi-agent Scenarios (2022)0.00