发表机构
Department of Computer Science and Communications Engineering; Waseda University（计算机科学与通信工程系; 早稻田大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究提出多智能体系统学习方法，能让智能体通过人类指令控制，未接指令的智能体可基于其他智能体行动补充工作。该方法扩展了可控性研究，实验表明使用此方法的智能体性能优于传统方法，能转向更好的合作结构。
AI 中文摘要
本研究提出了一种用于多智能体系统的学习方法，该方法允许智能体在学习后通过人类管理者指令进行控制，并使未接收到指令的智能体能够基于其他智能体的行动隐式地补充整体工作。使用深度学习的多智能体应用已展现出潜力，为实现广泛的社会应用，人类应能用简单方法控制已学习的智能体以应对环境和社会变化。以往一些研究虽旨在用简单指令控制智能体行为，但假设指令提供给所有智能体，既耗时又不利于设计更好的合作机制。理想情况下，特定智能体应接收关键行动指令，其他智能体自动完成剩余任务。所提方法扩展了多智能体深度强化学习中关于可控性的先前工作，使未接收到指令的智能体能自适应地补充被忽视的任务和区域。实验结果表明，使用该方法的智能体能够转向另一种合作结构，并比使用传统方法的智能体表现更好。
英文摘要
This study proposes a learning method for multi-agent systems that allows agents to be controlled through human manager instructions after learning and enables uninstructed agents to implicitly complement the overall work based on the actions of other agents. Multi-agent applications using deep learning have shown potential; thus, to achieve extensive social applications, humans should be able to control learned agents using simple methods to respond to environmental and social changes. Even without such changes, learned coordination often does not match the expectations of human managers, making it preferable to control coordination structures to match human intentions. Some studies have aimed to control agent behavior using simple instructions. However, they assumed that instructions are provided to all agents, which is time-consuming and not evident when designing a better cooperation regime. Ideally, specific agents should receive key action instructions, while others should automatically complete the remaining tasks. The proposed method, which extends previous work on controllability in multi-agent deep reinforcement learning, enables uninstructed agents to adaptively complement overlooked tasks and areas. The experimental results show that agents using the proposed method can shift to another cooperative structure and achieve better performance than those using conventional methods.
Comments8 pages, 14 figures, 24th IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2025)
