# Learning to Schedule Communication in Multi-agent Reinforcement Learning

arXiv: https://arxiv.org/abs/1902.01554 · HTML: https://mlsanity.com/paper/1902.01554/

## Key facts

- Cited by 93 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2019.

## Built on by (most cited first, 30 of 93)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Learning Multi-Agent Communication from Graph Modeling Perspective](https://mlsanity.com/paper/2405.08550/) (2024)
- [Multi-Agent Coordination across Diverse Applications: A Survey](https://mlsanity.com/paper/2502.14743/) (2025)
- [Multi-agent Reinforcement Learning: A Comprehensive Survey](https://mlsanity.com/paper/2312.10256/) (2023)
- [Learning Selective Communication for Multi-Agent Path Finding](https://mlsanity.com/paper/2109.05413/) (2021)
- [Internet of Agents: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2505.07176/) (2025)
- [A Review of Cooperation in Multi-agent Learning](https://mlsanity.com/paper/2312.05162/) (2023)
- [SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning](https://mlsanity.com/paper/2105.15013/) (2021)
- [Semantics-Native Communication with Contextual Reasoning](https://mlsanity.com/paper/2108.05681/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Collaborative Visual Navigation](https://mlsanity.com/paper/2107.01151/) (2021)
- [Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.03319/) (2021)
- [Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning](https://mlsanity.com/paper/2108.03803/) (2021)
- [Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration](https://mlsanity.com/paper/2301.03398/) (2023)
- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [Generative Emergent Communication: Large Language Model is a Collective World Model](https://mlsanity.com/paper/2501.00226/) (2024)
- [Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming](https://mlsanity.com/paper/2201.08484/) (2022)
- [Multi-Agent Coordination via Multi-Level Communication](https://mlsanity.com/paper/2209.12713/) (2022)
- [DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against Corruptions](https://mlsanity.com/paper/2412.10739/) (2024)
- [Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL](https://mlsanity.com/paper/2207.05683/) (2022)
- [Referential communication in heterogeneous communities of pre-trained visual deep networks](https://mlsanity.com/paper/2302.08913/) (2023)
- [T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration](https://mlsanity.com/paper/2401.10973/) (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)
- [DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2212.01619/) (2022)
- [Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks](https://mlsanity.com/paper/2309.06021/) (2023)
- [Consensus Learning for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2206.02583/) (2022)
- [SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2105.06228/) (2021)
- [FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems](https://mlsanity.com/paper/2201.11994/) (2022)
- [Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance](https://mlsanity.com/paper/2307.03119/) (2023)

## Abstract (arXiv)

Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents' interaction, where well-coordinated actions among the agents are crucial to achieve the target goal better at these tasks. One way to accelerate the coordination effect is to enable multiple agents to communicate with each other in a distributed manner and behave as a group. In this paper, we study a practical scenario when (i) the communication bandwidth is limited and (ii) the agents share the communication medium so that only a restricted number of agents are able to simultaneously use the medium, as in the state-of-the-art wireless networking standards. This calls for a certain form of communication scheduling. In that regard, we propose a multi-agent deep reinforcement learning framework, called SchedNet, in which agents learn how to schedule themselves, how to encode the messages, and how to select actions based on received messages. SchedNet is capable of deciding which agents should be entitled to broadcasting their (encoded) messages, by learning the importance of each agent's partially observed information. We evaluate SchedNet against multiple baselines under two different applications, namely, cooperative communication and navigation, and predator-prey. Our experiments show a non-negligible performance gap between SchedNet and other mechanisms such as the ones without communication and with vanilla scheduling methods, e.g., round robin, ranging from 32% to 43%.