# Succinct and Robust Multi-Agent Communication With Temporal Message Control

arXiv: https://arxiv.org/abs/2010.14391 · HTML: https://mlsanity.com/paper/2010.14391/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 29 of 29)

- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing](https://mlsanity.com/paper/2102.07475/) (2021)
- [Cooperative Exploration for Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2107.11444/) (2021)
- [Towards a Standardised Performance Evaluation Protocol for Cooperative MARL](https://mlsanity.com/paper/2209.10485/) (2022)
- [Learning Selective Communication for Multi-Agent Path Finding](https://mlsanity.com/paper/2109.05413/) (2021)
- [A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning](https://mlsanity.com/paper/2201.02932/) (2022)
- [Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning](https://mlsanity.com/paper/2108.03803/) (2021)
- [Context-aware Communication for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2312.15600/) (2023)
- [DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against Corruptions](https://mlsanity.com/paper/2412.10739/) (2024)
- [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)
- [Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2502.19717/) (2025)
- [Trust-based Consensus in Multi-Agent Reinforcement Learning Systems](https://mlsanity.com/paper/2205.12880/) (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)
- [Robust Communicative Multi-Agent Reinforcement Learning with Active Defense](https://mlsanity.com/paper/2312.11545/) (2023)
- [Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.06274/) (2022)
- [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)
- [Universally Expressive Communication in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2206.06758/) (2022)
- [Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2302.09605/) (2023)
- [LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.18077/) (2026)
- [Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth](https://mlsanity.com/paper/2306.10134/) (2023)
- [DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2308.09902/) (2023)
- [HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2606.29126/) (2026)
- [Low Entropy Communication in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.05055/) (2023)
- [DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training](https://mlsanity.com/paper/2409.07127/) (2024)
- [Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.03785/) (2026)
- [Finding the Weakest Link: Adversarial Attack against Multi-Agent Communications](https://mlsanity.com/paper/2605.13170/) (2026)

## Abstract (arXiv)

Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL). However, existing communication schemes often require agents to exchange an excessive number of messages at run-time under a reliable communication channel, which hinders its practicality in many real-world situations. In this paper, we present textitTemporal Message Control (TMC), a simple yet effective approach for achieving succinct and robust communication in MARL. TMC applies a temporal smoothing technique to drastically reduce the amount of information exchanged between agents. Experiments show that TMC can significantly reduce inter-agent communication overhead without impacting accuracy. Furthermore, TMC demonstrates much better robustness against transmission loss than existing approaches in lossy networking environments.