arXiv:2508. 13661v4 Announce Type: replace Abstract: Centralized Training with Decentralized Execution (CTDE) is the dominant paradigm in multi-agent reinforcement learning (MARL), enabling agents to act independently at test time while leveraging additional information during training.
By Maciej Wojtala, Bogusz Stefa\'nczyk, Dominik Bogucki, {\L}ukasz Lepak, Pawe{\l} Wawrzy\'nski
arXiv:2606. 29126v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) often relies on communication to mitigate partial observability, yet most existing protocols treat messages as flat dense vectors detached from the structure of the observations they summarize.
By Runze Zhao, Dongruo Zhou, Sumit Kumar Jha, Nathaniel D. Bastian, Ankit Shah
arXiv:2605. 18077v2 Announce Type: replace Abstract: Communication is a key component in multi-agent reinforcement learning (MARL) for mitigating partial observability, yet prior approaches often rely on inefficient information exchange or fail to transmit sufficient state information.
By Sangjun Bae, Yisak Park, Sanghyeon Lee, Seungyul Han
arXiv:2607. 17914v1 Announce Type: cross Abstract: Robust multi-agent coordination relies heavily on inter-agent communication, which is frequently disrupted by physical and environmental constraints in real-world deployments.
By Kemal Devrim Kafadar, Eren \"Ozaltun, Mahmud Efnan \c{S}anl{\i}, Feyza Orak, Emirhan Gazi, Kubilay Ka\u{g}an K\"om\"urc\"u, Naz{\i}m Kemal \"Ure
arXiv:2609.23269v1 Announce Type: cross
Abstract: In a decentralized multi-robot team under partial observability, the fact that decides a robot's next action is often visible only to a teammate. Exi...
By Howard Wang, Han Zheng, Cathy Wu
arXiv:2508. 06659v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) agents often struggle to generalize to new tasks and contexts without updating their parameters, mainly because their learned representations and policies are overfit to the specifics of their training environments.
By Fernando Martinez-Lopez, Tao Li, Yingdong Lu, Juntao Chen