# Learning Agent Communication under Limited Bandwidth by Message Pruning

arXiv: https://arxiv.org/abs/1912.05304 · HTML: https://mlsanity.com/paper/1912.05304/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 30 of 31)

- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Internet of Agents: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2505.07176/) (2025)
- [SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition](https://mlsanity.com/paper/2111.08857/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?](https://mlsanity.com/paper/2305.17352/) (2023)
- [Towards More Human-like AI Communication: A Review of Emergent Communication Research](https://mlsanity.com/paper/2308.02541/) (2023)
- [PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.08872/) (2022)
- [Communicating via Markov Decision Processes](https://mlsanity.com/paper/2107.08295/) (2021)
- [T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration](https://mlsanity.com/paper/2401.10973/) (2024)
- [DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2212.01619/) (2022)
- [Interpretable Learned Emergent Communication for Human-Agent Teams](https://mlsanity.com/paper/2201.07452/) (2022)
- [Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism](https://mlsanity.com/paper/2301.01919/) (2023)
- [Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation](https://mlsanity.com/paper/2305.05116/) (2023)
- [Who Needs to Know? Minimal Knowledge for Optimal Coordination](https://mlsanity.com/paper/2306.09309/) (2023)
- [Towards True Lossless Sparse Communication in Multi-Agent Systems](https://mlsanity.com/paper/2212.00115/) (2022)
- [Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.08482/) (2021)
- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [Robust Multi-agent Communication via Multi-view Message Certification](https://mlsanity.com/paper/2305.13936/) (2023)
- [Multi-Agent Reinforcement Learning with Communication-Constrained Priors](https://mlsanity.com/paper/2512.03528/) (2025)
- [Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2512.11179/) (2025)
- [Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2302.09605/) (2023)
- [NGENT: Next-Generation AI Agents Must Integrate Multi-Domain Abilities to Achieve Artificial General Intelligence](https://mlsanity.com/paper/2504.21433/) (2025)
- [Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth](https://mlsanity.com/paper/2306.10134/) (2023)
- [AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems](https://mlsanity.com/paper/2605.08813/) (2026)
- [Learning to Cooperate and Communicate Over Imperfect Channels](https://mlsanity.com/paper/2311.14770/) (2023)
- [Multi-agent Cooperative Games Using Belief Map Assisted Training](https://mlsanity.com/paper/2406.19477/) (2024)
- [Communication Learning in Multi-Agent Systems from Graph Modeling Perspective](https://mlsanity.com/paper/2411.00382/) (2024)
- [Learning Value of Information towards Joint Communication and Control in 6G V2X](https://mlsanity.com/paper/2505.06978/) (2025)
- [Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.03785/) (2026)
- [Heterogeneous Information-Bottleneck Coordination Graphs for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.17393/) (2026)

## Abstract (arXiv)

Communication is a crucial factor for the big multi-agent world to stay organized and productive. Recently, Deep Reinforcement Learning (DRL) has been applied to learn the communication strategy and the control policy for multiple agents. However, the practical emphlimited bandwidth in multi-agent communication has been largely ignored by the existing DRL methods. Specifically, many methods keep sending messages incessantly, which consumes too much bandwidth. As a result, they are inapplicable to multi-agent systems with limited bandwidth. To handle this problem, we propose a gating mechanism to adaptively prune less beneficial messages. We evaluate the gating mechanism on several tasks. Experiments demonstrate that it can prune a lot of messages with little impact on performance. In fact, the performance may be greatly improved by pruning redundant messages. Moreover, the proposed gating mechanism is applicable to several previous methods, equipping them the ability to address bandwidth restricted settings.