AI 中文总结
本文针对异构多智能体系统的资源受限任务委托问题，提出依赖交互历史的元策略委托框架与多维货币机制，基于多智能体强化学习实现高效协作与低成本任务完成。
AI 中文摘要
AI智能体将在未来决策系统中发挥日益重要的作用。本文研究由异构多智能体系统（MAS）构成的协作系统，其中成员具备不同能力与运行成本，探讨智能体如何相互委托任务，以在资源受限场景下高效完成特定研究任务。我们首先开发基于多智能体强化学习（MARL）的委托训练方法，使智能体能做出序列委托决策，同时最小化总执行成本；随后将该方法扩展至具有预设委托拓扑的MARL场景。此外，我们为MAS的协作与委托引入两个新框架：第一个框架提出，智能体的策略不仅依赖于基础马尔可夫决策过程的当前状态，还依赖于包含先前联合动作的交互历史，这种依赖历史的表述即使在完全可观测环境中也能提升协作性，而传统MARL方法通常将策略限制为仅依赖当前状态；第二个框架提出一种新颖的、潜在的多维货币机制，以促进MAS的协作与委托。
英文摘要
AI agents are expected to play an increasingly important role in future decision-making systems. In this paper, we consider collaborative systems composed of heterogeneous multi-agent systems (MAS), where their members have different capabilities and operating costs. We study how agents can delegate tasks to one another so that certain research tasks can be completed effectively under resource-constrained scenarios. We first develop a multi-agent reinforcement learning-based (MARL) delegation training that enables agents to make sequential delegation decisions while minimizing the total execution cost. We then extend this approach to MARL with prescribed delegation topologies. Furthermore, we introduce two new frameworks for collaboration and delegation in multi-agent systems. The first framework proposes that an agent's policy depends not only on the current state of the underlying Markov decision process but also on the interaction history, including previous joint actions. This history-dependent formulation can improve coordination even in fully observable environments, where conventional MARL methods typically restrict policies to depend only on the current state. The second framework proposes a novel, potentially multi-dimensional monetary mechanism to facilitate the collaboration and delegation for MAS.
