Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
Shuo Tang, Rui Ye, Chenxin Xu, Xiaowen Dong, Siheng Chen, Yanfeng Wang
TL;DR
This paper introduces DeLAMA, a decentralized multi-agent learning algorithm that dynamically learns collaboration strategies and adapts to changing tasks, significantly improving efficiency and accuracy in multi-agent systems.
Contribution
DeLAMA is the first decentralized lifelong learning algorithm with dynamic collaboration graphs and a memory unit, enabling autonomous relationship learning and adaptation without external priors.
Findings
Achieved 98.80% reduction in MSE.
Improved classification accuracy by 188.87%.
Verified communication efficiency with few rounds.
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…
Peer Reviews
Decision·Submitted to ICLR 2026
The study appears to be theoretically sound and technically solid.
The limitation of this study is the simplicity of its experimental design. The evaluation is restricted to image classification and simulated robot mapping tasks. Assessing performance in real-world scenarios would provide a more comprehensive validation.
1.DeLAMA is supported by comprehensive theoretical proofs, ensuring its stability, efficiency, and convergence. 2.Uniquely combines decentralized learning, lifelong learning, and dynamic graph learning into a single framework. This innovative approach eliminates the need for a central server and adapts to real-time task changes, offering a versatile solution for multi-agent systems. 3.The framework enables agents to autonomously adjust their collaboration relationships based on evolving tasks.
1.The experimental results do not demonstrate significant or optimal performance improvements compared to existing methods, raising questions about the effectiveness of DeLAMA in achieving superior outcomes. 2.The baseline methods used for comparison are relatively old and not state-of-the-art, which may not provide a fair or comprehensive evaluation of DeLAMA`s capabilities. 3.The experiments are conducted on smaller datasets (e.g., MNIST, CIFAR-10), what about larger or more complex datasets l
1. The paper is well-organized and well-written and can be followed straightforwardly. 2. The idea of enabling autonomously learning collaboration structures and adapting continuously to dynamic tasks is interesting, novel, and of practical importance. 3.Theoretical results are sound and demonstrate convergence guarantee which is very important in decentralized learning settings. 4. Experiments are extensive beyond just demonstrating that DeLAMA works. Various informative analytic and ablati
1. The optimization involves alternating convex searches, Taylor approximations, and graph Laplacian regularization, then wraps them in algorithm unrolling. This pipeline is computationally heavy and might not be applicable for a lifelong learning setting where learning speed is important. This is different from convergence because we may not many iterations but each each iteration can be very time-consuming. 2. Although theoretically efficient, the Newton-based graph inference and Jacobi-style
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · Intelligent Tutoring Systems and Adaptive Learning · Innovative Teaching and Learning Methods