论文/arXiv 2610.10302·21 天前

持续图多智能体强化学习

针对持续多智能体强化学习中的结构迁移与遗忘，提出CGMARL框架和FROG方法，并在GRAFO基准验证性能提升。

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原文链接：Continual Graph Multi-Agent Reinforcement Learning（arXiv 2610.10302 · 发布日期：2026-10-07）

Continual Graph Multi-Agent Reinforcement Learning

针对持续多智能体强化学习中的结构迁移与遗忘，提出CGMARL框架和FROG方法，并在GRAFO基准验证性能提升。

核心要点

• CMARL需在任务序列中适应新任务并保留旧任务能力，但现有方法缺少利用任务结构信息的机制。
• CGMARL将任务序列映射为带属性图序列，每个图建模任务特定结构并决定环境动态和智能体数量。
• 提出GRAFO作为首个CGMARL基准，并展示该设置中遗忘现象如何产生。
• FROG使用冻结图骨干保留过去结构信息，与现有持续学习方法结合后在多个CGMARL场景显著提升表现。

验证范围与局限：验证限于GRAFO基准上的多个CGMARL场景。

英文摘要 (Abstract)

In Continual Multi-Agent Reinforcement Learning (CMARL), agents learn cooperative policies across sequences of tasks, aiming to adapt effectively to new tasks while preserving the ability to solve previously encountered ones. In many applications, tasks differ in their underlying structure, which can represent, for example, distinct operational conditions or target configurations (e.g., different network topologies in power grids or arrangements in formation control). Existing CMARL methods lack dedicated mechanisms to leverage this structural information when learning new tasks, failing to promote transfer and mitigate forgetting. To fill this gap, we propose Continual Graph Multi-Agent Reinforcement Learning (CGMARL), a novel framework for CMARL problems in which task sequences are mapped into a series of attributed graphs, each modeling a task-specific structure. In CGMARL, each graph determines the environment dynamics (next states and/or rewards) and the number of agents for the corresponding task. Then, we present Graph-based Formation (GRAFO), the first CGMARL benchmark, and show how forgetting arises in this setting. Finally, to address this limitation, we propose Frozen Graph Encoder (FROG), a method that relies on a frozen graph backbone to preserve past structural information in graph-based CMARL policies. Experiments on GRAFO show that pairing FROG with existing CL methods substantially improves performance on multiple CGMARL scenarios.

作者：Tommaso Marzi, Ahmed Hendawy, Jan Peters, Carlo D’Eramo, Andrea Cini, Cesare Alippi

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.10302：Continual Graph Multi-Agent Reinforcement Learning访问 2026-10-21