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. Code is available at
this https URL

提出了 DeLAMA 算法，它是一种具有动态协作图的分散式多智能体协同学习算法，能够促进自主协作关系学习和适应动态任务，实现了 MSE 减少 98.80% 和分类准确率提高 188.87%，为未来建立智能、分散式和动态多智能体系统提供了基础技术。