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 https://github.com/ShuoTang123/DeLAMA.

翻译：去中心化与终身自适应多智能体协同学习旨在无需中央服务器的情况下增强多智能体间的协作，每个智能体需随时间变化解决不同任务。为实现高效协作，智能体需要：i) 以去中心化方式自主识别有益的协作关系；ii) 适应动态变化的观测任务。本文提出DeLAMA，一种基于动态协作图的去中心化多智能体终身协同学习算法。为促进自主协作关系学习，我们提出一种去中心化图结构学习算法，无需外部先验知识。为适应动态任务，我们设计了记忆单元以捕获智能体累积的学习历史与知识，同时保持有限存储消耗。为进一步增强系统的表达能力与计算效率，我们应用了算法展开技术，融合了数学优化与神经网络的各自优势，使智能体能够通过训练任务的监督信号"学会协作"。理论分析验证了在少量通信轮次下智能体间协作具有通信高效性。实验结果证明该方法能有效促进协作策略发现并适应动态学习场景，均方误差降低98.80%，分类准确率提升188.87%。我们期望该工作可为未来构建智能、去中心化、动态多智能体系统奠定基础技术。代码开源于https://github.com/ShuoTang123/DeLAMA。