Title: Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
Primary area: learning on graphs and other geometries & topologies
Keywords: Multi-agent systems, collaborative learning, graph learning, algorithm unrolling

Abstract: Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents
without a central server, with each agent solving varied tasks over time. To achieve efficient collaboration, agents should: i) autonomously
identify beneficial collaborative relationships in a decentralized manner; and ii) adapt to dynamically changing task observations. In this
paper, we propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs. To
promote autonomous collaboration relationship learning, we propose a decentralized graph structure learning algorithm, eliminating the
need for external priors. To facilitate adaptation to dynamic tasks, we design a memory unit to capture the agents’ accumulated learning
history and knowledge, while preserving finite storage consumption. To further augment the system’s expressive capabilities and
computational efficiency, we apply algorithm unrolling, leveraging the advantages of both mathematical optimization and neural networks.
This allows the agents to ‘learn to collaborate’ through the supervision of training tasks. Our theoretical analysis verifies that inter-agent
collaboration is communication efficient under a small number of communication rounds. The experimental results verify its ability to
facilitate the discovery of collaboration strategies and adaptation to dynamic learning scenarios, achieving a 98.80% reduction in MSE and
a 188.87% improvement in classification accuracy. We expect our work can serve as a foundational technique to facilitate future works
towards an intelligent, decentralized, and dynamic multi-agent system.