Robust and Efficient Communication for Multi-Agent Learning

arXiv:2609.15361 · cs.LG, cs.AI, cs.MA · Submitted 2026-09-14 · Read on arXiv

cs.LG, cs.AI, cs.MA

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 12 pages, 11 figures

Code: https://github.com/rafaelmp2/marc-marl

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 21× in this paper · explained in 4 episodes
• Multi-Agent Reinforcement Learning — 12× in this paper
• Cooperation — 8× in this paper · explained in 2 episodes
• Discrete Cosine Transform — 5× in this paper
• Core — 4× in this paper · explained in 2 episodes
• Multi-Agent Regularized Communication — 4× in this paper
• Regularization — 4× in this paper · explained in 3 episodes
• Dec-POMDP — 3× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 3× in this paper · explained in 2 episodes
• Physical Constraints — 3× in this paper · explained in 7 episodes
• Q-learning — 3× in this paper · explained in 3 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Centralised Training with Decentralised Execution — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Multi-Agent Reinforcement Learning (MARL) — 2× in this paper · explained in 4 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Agent Architectures — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Value Decomposition Networks — 1× in this paper

Sources

• Multi-Agent Intention Sharing via Leader-Follower Forest
• Learning to Communicate with Deep Multi-Agent Reinforcement Learning
• TarMAC: Targeted Multi-Agent Communication
• Capacity, Bandwidth, and Compositionality in Emergent Language Learning
• Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
• QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
• Learning Attentional Communication for Multi-Agent Cooperation
• Emergence of Grounded Compositional Language in Multi-Agent Populations
• Networked Multi-Agent Reinforcement Learning with Emergent Communication
• Learning to cooperate: Emergent communication in multi-agent navigation
• Learning to Schedule Communication in Multi-agent Reinforcement Learning
• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
• The StarCraft Multi-Agent Challenge
• Deep Coordination Graphs

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