# Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1712.08266 · HTML: https://mlsanity.com/paper/1712.08266/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2017.

## Built on by (most cited first, 8 of 8)

- [HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging](https://mlsanity.com/paper/2102.00824/) (2021)
- [Multi-Task Federated Reinforcement Learning with Adversaries](https://mlsanity.com/paper/2103.06473/) (2021)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks](https://mlsanity.com/paper/2112.03465/) (2021)
- [Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level](https://mlsanity.com/paper/2110.13621/) (2021)
- [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024)
- [Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System](https://mlsanity.com/paper/2402.09729/) (2024)
- [Large Stepsizes Federated Learning on Logistic Regression with Linearly Separable Data: The Case of Heterogeneous Devices](https://mlsanity.com/paper/2610.05915/) (2026)

## Abstract (arXiv)

We present a framework combining hierarchical and multi-agent deep reinforcement learning approaches to solve coordination problems among a multitude of agents using a semi-decentralized model. The framework extends the multi-agent learning setup by introducing a meta-controller that guides the communication between agent pairs, enabling agents to focus on communicating with only one other agent at any step. This hierarchical decomposition of the task allows for efficient exploration to learn policies that identify globally optimal solutions even as the number of collaborating agents increases. We show promising initial experimental results on a simulated distributed scheduling problem.