arXiv AI June 2, 2026 By Sangjun Bae, Yisak Park, Sanghyeon Lee, Seungyul Han

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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

agents diffusion reinforcement-learning

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

agents reinforcement-learning

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

agents reinforcement-learning safety

arXiv AI

Jul 1

HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation

arXiv:2606. 30966v1 Announce Type: new Abstract: Formal specification is a powerful tool to guide the learning process and provides significant advantages over reward shaping: (1) mathematical rigor; (2) expressiveness to specify objectives and constraints, and (3) the ability to define tactics to achieve objectives.

By Arshia Rafieioskouei, Tzu-Han Hsu, Matthew Lucas, Borzoo Bonakdarpour

agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 23

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

By Taisuke Takayama, Naoto Yoshida, Tadahiro Taniguchi

agents reinforcement-learning

arXiv Machine Learning

Jun 16

Probing Dec-POMDP Reasoning in Cooperative MARL

arXiv:2602. 20804v2 Announce Type: replace Abstract: Cooperative multi-agent reinforcement learning (MARL) is typically framed as a decentralised partially observable Markov decision process (Dec-POMDP), a setting whose hardness stems from two key challenges: partial observability and decentralised coordination.

By Kale-ab Tessera, Leonard Hinckeldey, Riccardo Zamboni, David Abel, Amos Storkey

agents reinforcement-learning benchmarks

Related stories

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

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

HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning

HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation

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

Probing Dec-POMDP Reasoning in Cooperative MARL