Cover Image

Learning to Communicate Through Implicit Communication Channels

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Han, Chen, Binbin, Zhang, Tieying, Wang, Baoxiang
Format:	Preprint
Published:	2024
Subjects:	Multiagent Systems Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Carbon Market Simulation with Adaptive Mechanism Design
by: Wang, Han, et al.
Published: (2024)

Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning
by: Li, Xinran, et al.
Published: (2025)

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
by: Bae, Sangjun, et al.
Published: (2026)

Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control
by: Gu, Hankang, et al.
Published: (2025)

Enabling Agents to Communicate Entirely in Latent Space
by: Du, Zhuoyun, et al.
Published: (2025)

Thought Communication in Multiagent Collaboration
by: Zheng, Yujia, et al.
Published: (2025)

Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication
by: Bastola, Ashish, et al.
Published: (2025)

Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding
by: Vyaltsev, Valeriy, et al.
Published: (2026)

Communicating Sound Through Natural Language
by: Rossi, Emanuele, et al.
Published: (2026)

KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems
by: Ye, Hancheng, et al.
Published: (2025)

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems
by: Yang, Huchen, et al.
Published: (2026)

A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication
by: Cuzin-Rambaud, Valentin, et al.
Published: (2026)

Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics
by: Siedler, Philipp Dominic
Published: (2023)

MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
by: Lin, Huawei, et al.
Published: (2026)

Learning to Negotiate via Voluntary Commitment
by: Zhu, Shuhui, et al.
Published: (2025)

Multi-Agent Deep Q-Network with Layer-based Communication Channel for Autonomous Internal Logistics Vehicle Scheduling in Smart Manufacturing
by: Feizabadi, Mohammad, et al.
Published: (2024)

GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems
by: Chen, Hongjiang, et al.
Published: (2026)

KVComm: Enabling Efficient LLM Communication through Selective KV Sharing
by: Shi, Xiangyu, et al.
Published: (2025)

GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning
by: Yu, Xiaoyang, et al.
Published: (2023)

Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects
by: Wang, Xihuai, et al.
Published: (2022)

EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks
by: Ray, Abir
Published: (2025)

A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems
by: Chen, Siyuan, et al.
Published: (2024)

Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning
by: Khan, Muhammad Al-Zafar, et al.
Published: (2025)

Learning Network Sheaves for AI-native Semantic Communication
by: Grimaldi, Enrico, et al.
Published: (2025)

Implicit Repair with Reinforcement Learning in Emergent Communication
by: Vital, Fábio, et al.
Published: (2025)

Communication-Free Collective Navigation for a Swarm of UAVs via LiDAR-Based Deep Reinforcement Learning
by: Choi, Myong-Yol, et al.
Published: (2026)

Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
by: Tang, Shuo, et al.
Published: (2024)

M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality
by: Wang, Ziyan, et al.
Published: (2025)

Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning
by: Liu, Lunjun, et al.
Published: (2024)

Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm
by: Kim, Hyeonjun, et al.
Published: (2025)

${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
by: Chen, Dingyang, et al.
Published: (2023)

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
by: Lee, Sunwoo, et al.
Published: (2026)

Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication
by: Kölle, Michael, et al.
Published: (2026)

Real-time Adapting Routing (RAR): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Models
by: Vasilevski, Kirill, et al.
Published: (2024)

Learning Communication Between Heterogeneous Agents in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
by: Popa, Alex, et al.
Published: (2026)

Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints
by: Qiao, Dan, et al.
Published: (2024)

Language-Driven Coordination and Learning in Multi-Agent Simulation Environments
by: Li, Zhengyang, et al.
Published: (2025)

PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making
by: Light, Jonathan, et al.
Published: (2024)

Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
by: Yang, Jiaxi, et al.
Published: (2025)

QSIM: Mitigating Overestimation in Multi-Agent Reinforcement Learning via Action Similarity Weighted Q-Learning
by: Li, Yuanjun, et al.
Published: (2026)