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Consequently, many cooperative multi-agent reinforcement learning (MARL) methods have incorporated DOL mechanisms to improve cooperation among agents. However, the lack of benchmark tasks specifically designed to evaluate and promote DOL and cooperation has limited the effective development and deployment of such mechanisms in cooperative MARL. This gap between current cooperative MARL methods and practical applications underscores the need for evaluation tasks that explicitly require DOL and cooperation. To address this gap, we propose the Composite Tasks Challenge (CTC), a suite of tasks explicitly designed to require both DOL and cooperation for successful task completion. The CTC tasks are constructed based on two core design principles: 1) DOL is a necessary condition for task success; 2) Failure in any atom","title":"CTC: The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning","url":"https://arxiv.org/abs/2502.00345","vendor":"arxiv_cs_ai"},"summary":"arXiv:2502.00345v2 Announce Type: replace-cross \nAbstract: The critical role of division of labor (DOL) in enhancing cooperation is well-recognized in real-world applications. Consequently, many cooperative multi-agent reinforcement learning (MARL) methods have incorporated DOL mechanisms to improve cooperation among agents. However, the lack of benchmark tasks specifically designed to evaluate and promote DOL and cooperation has limited the effective development and deployment of such mechanisms in cooperative MARL. This gap between current cooperative MARL methods and practical applications underscores the need for evaluation tasks that explicitly require DOL and cooperation. To address this gap, we propose the Composite Tasks Challenge (CTC), a suite of tasks explicitly designed to require both DOL and cooperation for successful task completion. 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