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

The paper introduces a framework combining variational autoencoders and reinforcement learning to dynamically adjust network structures in multi-agent systems, balancing performance and resource usage. It addresses the vast action space of network structures by encoding them into a latent space. Evaluated on the OpenAI particle environment, the method outperforms baselines, offering superior performance and revealing insights into learned behaviors. This innovation is crucial for managing complex, decentralized systems with multiple autonomous agents.