arXiv Machine Learning By Taisuke Takayama, Naoto Yoshida, Tadahiro Taniguchi

Dreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2607. 19809v1 Announce Type: cross Abstract: In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability.

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arXiv Machine Learning
Aug 6

Communication-Enhanced Tutoring for Efficient Decentralized Multi-Agent Reinforcement Learning

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 AI
Jun 30

HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning

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 Machine Learning
Jul 21

Value-Aware Prediction for Robust Multi-Agent Coordination Under Communication Loss

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