# Event-Triggered Multi-agent Reinforcement Learning with Communication under Limited-bandwidth Constraint

arXiv: https://arxiv.org/abs/2010.04978 · HTML: https://mlsanity.com/paper/2010.04978/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against Corruptions](https://mlsanity.com/paper/2412.10739/) (2024)
- [The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs](https://mlsanity.com/paper/2602.11583/) (2026)
- [Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges](https://mlsanity.com/paper/2305.10091/) (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)
- [Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.06274/) (2022)
- [Universally Expressive Communication in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2206.06758/) (2022)
- [Learning what to say and how precisely: Efficient Communication via Differentiable Discrete Communication Learning](https://mlsanity.com/paper/2511.01554/) (2025)
- [CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2112.10374/) (2021)

## Abstract (arXiv)

Communicating with each other in a distributed manner and behaving as a group are essential in multi-agent reinforcement learning. However, real-world multi-agent systems suffer from restrictions on limited-bandwidth communication. If the bandwidth is fully occupied, some agents are not able to send messages promptly to others, causing decision delay and impairing cooperative effects. Recent related work has started to address the problem but still fails in maximally reducing the consumption of communication resources. In this paper, we propose Event-Triggered Communication Network (ETCNet) to enhance the communication efficiency in multi-agent systems by sending messages only when necessary. According to the information theory, the limited bandwidth is translated to the penalty threshold of an event-triggered strategy, which determines whether an agent at each step sends a message or not. Then the design of the event-triggered strategy is formulated as a constrained Markov decision problem, and reinforcement learning finds the best communication protocol that satisfies the limited bandwidth constraint. Experiments on typical multi-agent tasks demonstrate that ETCNet outperforms other methods in terms of the reduction of bandwidth occupancy and still preserves the cooperative performance of multi-agent systems at the most.