用变分自编码器+强化学习动态调优网络结构，省资源还提性能
Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learning
- 用VAE编码网络结构，通过控制隐空间实现对复杂动作空间的高效管理
- 在多智能体场景下，相比基线方法性能更优，资源利用更合理
- 可揭示隐藏策略，适合研究分布式系统优化与资源调度的开发者
本文提出一种融合变分自编码器（VAE）与强化学习（RL）的框架，通过动态调整网络结构，在多智能体系统中平衡系统性能与资源消耗。该方法的核心创新在于处理网络结构带来的巨大动作空间：通过将网络结构编码至隐空间，并结合深度强化学习进行控制。在修改后的OpenAI粒子环境下的多种场景中评估表明，该方法不仅优于基线模型，还能通过学习到的行为揭示出有趣的策略与洞见。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。