# Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport

arXiv: https://arxiv.org/abs/2103.15260 · HTML: https://mlsanity.com/paper/2103.15260/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [R-MADDPG for Partially Observable Environments and Limited Communication](https://mlsanity.com/paper/2002.06684/) (2020)
- [Deep Reinforcement Learning for Event-Triggered Control](https://mlsanity.com/paper/1809.05152/) (2018)
- [Learning Event-triggered Control from Data through Joint Optimization](https://mlsanity.com/paper/2008.04712/) (2020)

## Built on by (most cited first, 5 of 5)

- [Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient](https://mlsanity.com/paper/2210.05367/) (2022)
- [Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport](https://mlsanity.com/paper/2404.02362/) (2024)
- [Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learning](https://mlsanity.com/paper/2410.23393/) (2024)
- [Communication-Control Codesign for Large-Scale Wireless Networked Control Systems](https://mlsanity.com/paper/2410.11316/) (2024)
- [Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning](https://mlsanity.com/paper/2602.16353/) (2026)

## Abstract (arXiv)

In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control; these methods cannot decide the timing of communication and can only work with fixed-rate communications. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations.