# Context-aware Communication for Multi-agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2312.15600 · HTML: https://mlsanity.com/paper/2312.15600/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 25 Dec 2023.

## Builds on (most cited first, 18 of 18)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [TarMAC: Targeted Multi-Agent Communication](https://mlsanity.com/paper/1810.11187/) (2018)
- [Learning Attentional Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/1805.07733/) (2018)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games](https://mlsanity.com/paper/1703.10069/) (2017)
- [Learning Nearly Decomposable Value Functions Via Communication Minimization](https://mlsanity.com/paper/1910.05366/) (2019)
- [Multi-Agent Game Abstraction via Graph Attention Neural Network](https://mlsanity.com/paper/1911.10715/) (2019)
- [Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.04402/) (2021)
- [Learning Individually Inferred Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/2006.06455/) (2020)
- [Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control](https://mlsanity.com/paper/1909.02682/) (2019)
- [A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment](https://mlsanity.com/paper/2312.01058/) (2023)
- [Succinct and Robust Multi-Agent Communication With Temporal Message Control](https://mlsanity.com/paper/2010.14391/) (2020)
- [Scaled Autonomy: Enabling Human Operators to Control Robot Fleets](https://mlsanity.com/paper/1910.02910/) (2019)
- [Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization](https://mlsanity.com/paper/2202.01334/) (2022)

## Built on by (most cited first, 13 of 13)

- [Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2502.19717/) (2025)
- [Adaptive Context Length Optimization with Low-Frequency Truncation for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2510.26389/) (2025)
- [Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms?](https://mlsanity.com/paper/2510.03257/) (2025)
- [Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.17062/) (2026)
- [Unifying Agent Interaction and World Information for Multi-agent Coordination](https://mlsanity.com/paper/2509.25550/) (2025)
- [Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus](https://mlsanity.com/paper/2604.13472/) (2026)
- [AgentComm-Bench: Stress-Testing Cooperative Embodied AI Under Latency, Packet Loss, and Bandwidth Collapse](https://mlsanity.com/paper/2603.20285/) (2026)
- [HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2606.29126/) (2026)
- [DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training](https://mlsanity.com/paper/2409.07127/) (2024)
- [Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds](https://mlsanity.com/paper/2506.09335/) (2025)
- [PC2P: Multi-Agent Path Finding via Personalized-Enhanced Communication and Crowd Perception](https://mlsanity.com/paper/2601.03301/) (2026)
- [Decoupling Communication from Policy: Robust MARL under Bandwidth Constraints](https://mlsanity.com/paper/2605.21085/) (2026)
- [WONDER: A Radio World Model-based Negotiation Framework for Multi-Agent UAV Coverage Optimization](https://mlsanity.com/paper/2608.16955/) (2026)

## Abstract (arXiv)

Effective communication protocols in multi-agent reinforcement learning (MARL) are critical to fostering cooperation and enhancing team performance. To leverage communication, many previous works have proposed to compress local information into a single message and broadcast it to all reachable agents. This simplistic messaging mechanism, however, may fail to provide adequate, critical, and relevant information to individual agents, especially in severely bandwidth-limited scenarios. This motivates us to develop context-aware communication schemes for MARL, aiming to deliver personalized messages to different agents. Our communication protocol, named CACOM, consists of two stages. In the first stage, agents exchange coarse representations in a broadcast fashion, providing context for the second stage. Following this, agents utilize attention mechanisms in the second stage to selectively generate messages personalized for the receivers. Furthermore, we employ the learned step size quantization (LSQ) technique for message quantization to reduce the communication overhead. To evaluate the effectiveness of CACOM, we integrate it with both actor-critic and value-based MARL algorithms. Empirical results on cooperative benchmark tasks demonstrate that CACOM provides evident performance gains over baselines under communication-constrained scenarios. The code is publicly available at https://github.com/LXXXXR/CACOM.