arXiv AI August 12, 2026 By Ahmad Farooq, Kamran Iqbal

Bandwidth-Efficient Multi-Agent Communication through Information Bottleneck and Vector Quantization

arXiv:2602. 02035v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning systems deployed in real-world robotics applications face severe communication constraints that significantly impact coordination effectiveness.

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arXiv AI

Sep 15

Robust and Efficient Communication for Multi-Agent Learning

arXiv:2609.15361v1 Announce Type: cross Abstract: Effective communication is a cornerstone of distributed intelligence in Multi-Agent Reinforcement Learning (MARL), yet ensuring that generated messag...

By Rafael Pina, Varuna De Silva, Corentin Artaud

agents reinforcement-learning robotics benchmarks

arXiv AI

Jun 2

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.

By Sangjun Bae, Yisak Park, Sanghyeon Lee, Seungyul Han

llms agents reinforcement-learning benchmarks

Hugging Face Trending Papers

Jun 17

Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation

Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in drone swarms and autonomous vehicle fleets. We present the first end-to-end framework for safety verification of learned multi-agent communication policies through policy abstraction: neural policies are distilled into interpretable decision trees, then formally verified, with empirical validation confirming that verified safety properties transfer to original networks.

agents reinforcement-learning robotics efficiency safety

arXiv Machine Learning

Jun 9

A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles

arXiv:2508. 00917v2 Announce Type: replace-cross Abstract: Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in complex environments.

By Jiayuan Wang, Farhad Pourpanah, Q. M. Jonathan Wu, Ning Zhang

agents

arXiv Machine Learning

Aug 7

Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment

arXiv:2603. 16141v2 Announce Type: replace-cross Abstract: Autonomous Unmanned Aerial Vehicle (UAV) swarms are increasingly used as rapidly deployable aerial relays and sensing platforms, yet practical deployments must operate under partial observability and intermittent peer-to-peer connectivity.

By Enguang Fan, Yifan Chen, Zihan Shan, Klara Nahrstedt, Matthew Caesar, Jae Kim

agents reinforcement-learning

arXiv AI

Jun 2

Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters

arXiv:2606. 02107v1 Announce Type: cross Abstract: This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control.

By Youssef Mahran, Zeyad Gamal, Aamir Ahmad, Ayman El-Badawy

agents reinforcement-learning fine-tuning

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Robust and Efficient Communication for Multi-Agent Learning

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning

Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation

A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles

Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment

Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters