# Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things

arXiv: https://arxiv.org/abs/2401.12914 · HTML: https://mlsanity.com/paper/2401.12914/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multi-agent reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Placed in

- Deep reinforcement learning methods / [Multi-agent reinforcement learning](https://mlsanity.com/deeprl/multi-agent-reinforcement-learning/)

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications](https://mlsanity.com/paper/2008.06701/) (2020)
- [Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Access](https://mlsanity.com/paper/2007.09948/) (2020)
- [The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.07144/) (2021)
- [Learning Generalized Wireless MAC Communication Protocols via Abstraction](https://mlsanity.com/paper/2206.06331/) (2022)
- [Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling](https://mlsanity.com/paper/2206.03844/) (2022)

## Built on by (most cited first, 1 of 1)

- [Learning Power Control Protocol for In-Factory 6G Subnetworks](https://mlsanity.com/paper/2505.05967/) (2025)

## Abstract (arXiv)

In this paper, we leverage a multi-agent reinforcement learning (MARL) framework to jointly learn a computation offloading decision and multichannel access policy with corresponding signaling. Specifically, the base station and industrial Internet of Things mobile devices are reinforcement learning agents that need to cooperate to execute their computation tasks within a deadline constraint. We adopt an emergent communication protocol learning framework to solve this problem. The numerical results illustrate the effectiveness of emergent communication in improving the channel access success rate and the number of successfully computed tasks compared to contention-based, contention-free, and no-communication approaches. Moreover, the proposed task offloading policy outperforms remote and local computation baselines.