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.

翻译：本文提出了一种集成变分自编码器（VAE）与强化学习（RL）的框架，通过动态调整多智能体系统随时间演化的网络结构，以平衡系统性能与资源消耗。该方法的一项关键创新在于其能够处理网络结构庞大的动作空间。这是通过结合变分自编码器与深度强化学习，对从网络结构编码得到的潜在空间进行控制来实现的。所提方法在多种场景下的改进版OpenAI粒子环境中进行评估，不仅展现出相较于基线方法的优越性能，还通过学习到的行为揭示了有趣的策略与洞见。