Advancing Multi-Agent Systems with Scalable Learning and Control

Efficient and resilient coordination among autonomous agents plays an important role in various domains such as energy management, robotic swarms, autonomous vehicles and beyond. As these systems grow in complexity and scale, the challenge of achieving optimal coordination becomes increasingly difficult. The first part of the talk will focus on tackling scalability issues by leveraging network structure. I will discuss how leveraging spatially-exponential decaying (SED) structures in networked systems enables scalable and near-optimal decentralized control. We will show that under certain mild assumption, the optimal controller also demonstrates a similar SED structure, which leads to theoretical guarantees for efficient distributed strategies and offers practical insights for large-scale coordination. The second part of the talk will focus on efficient Nash equilibrium seeking for multi-agent systems. The key element guiding our approach is the concept of ‘marginalized environment’ which provides more flexible algorithm design and tractable theoretical analysis. Building upon this notion, we introduce a policy-gradient algorithm with provable sample complexity guarantees. Lastly I will briefly present our work on several applications such as green buildings and multi-robotics and outline a roadmap for future work including closing the loop between theory development and applications for AI-enabled multi-agent societal systems.