标题： 通过技能图的多任务多智能体强化学习 显示英文标题

标题： Multi-Task Multi-Agent Reinforcement Learning via Skill Graphs

摘要： 多任务多智能体强化学习（MT-MARL）最近受到关注，因为它有望提高MARL在多个任务中的适应能力。 然而，现有多任务学习方法在处理复杂问题时存在挑战，因为它们无法处理不相关任务，并且知识迁移能力有限。 在本文中，我们提出了一种分层方法，以有效解决这些挑战。 高层模块利用技能图，而低层模块采用标准的MARL算法。 我们的方法有两个贡献。 首先，我们在不相关任务的背景下考虑MT-MARL问题，扩展了MTRL的范围。 其次，技能图被用作标准分层方法的上层，训练独立于下层，有效地处理不相关任务并增强知识迁移能力。 进行了大量实验以验证这些优势，并证明所提出的方法优于最新的分层MAPPO算法。 视频和代码可在https://github.com/WindyLab/MT-MARL-SG获得 显示英文摘要

摘要： Multi-task multi-agent reinforcement learning (MT-MARL) has recently gained attention for its potential to enhance MARL's adaptability across multiple tasks. However, it is challenging for existing multi-task learning methods to handle complex problems, as they are unable to handle unrelated tasks and possess limited knowledge transfer capabilities. In this paper, we propose a hierarchical approach that efficiently addresses these challenges. The high-level module utilizes a skill graph, while the low-level module employs a standard MARL algorithm. Our approach offers two contributions. First, we consider the MT-MARL problem in the context of unrelated tasks, expanding the scope of MTRL. Second, the skill graph is used as the upper layer of the standard hierarchical approach, with training independent of the lower layer, effectively handling unrelated tasks and enhancing knowledge transfer capabilities. Extensive experiments are conducted to validate these advantages and demonstrate that the proposed method outperforms the latest hierarchical MAPPO algorithms. Videos and code are available at https://github.com/WindyLab/MT-MARL-SG