标题： 从独奏到交响乐：通过单智能体演示协调多智能体协作 显示英文标题

标题： From Solo to Symphony: Orchestrating Multi-Agent Collaboration with Single-Agent Demos

摘要： 从零开始训练多智能体强化学习（MARL）中的智能体团队效率非常低，就像让初学者在没有单独练习的情况下一起演奏交响乐一样。现有的方法，如离线或可迁移的MARL，可以减轻这一负担，但它们仍然依赖于成本高昂的多智能体数据，这往往成为瓶颈。相比之下，在许多重要场景中，如协作编码、家庭合作和搜索与救援，单独的经验更容易获得。为了释放其潜力，我们提出了Solo-to-Collaborative RL（SoCo），一种将单独知识转移到协作学习中的框架。SoCo首先从单独演示中预训练一个共享的单独策略，然后通过一种结合类似MoE的门控选择器和动作编辑器的策略融合机制，在多智能体训练过程中对其进行适应。在多种协作任务上的实验表明，SoCo显著提高了基础算法的训练效率和性能。这些结果表明，单独演示为多智能体数据提供了一种可扩展且有效的补充，使协作学习更加实用和广泛应用。 显示英文摘要

摘要： Training a team of agents from scratch in multi-agent reinforcement learning (MARL) is highly inefficient, much like asking beginners to play a symphony together without first practicing solo. Existing methods, such as offline or transferable MARL, can ease this burden, but they still rely on costly multi-agent data, which often becomes the bottleneck. In contrast, solo experiences are far easier to obtain in many important scenarios, e.g., collaborative coding, household cooperation, and search-and-rescue. To unlock their potential, we propose Solo-to-Collaborative RL (SoCo), a framework that transfers solo knowledge into cooperative learning. SoCo first pretrains a shared solo policy from solo demonstrations, then adapts it for cooperation during multi-agent training through a policy fusion mechanism that combines an MoE-like gating selector and an action editor. Experiments across diverse cooperative tasks show that SoCo significantly boosts the training efficiency and performance of backbone algorithms. These results demonstrate that solo demonstrations provide a scalable and effective complement to multi-agent data, making cooperative learning more practical and broadly applicable.