# Reinforcement Learning based Distributed Control of Dissipative Networked Systems

arXiv: https://arxiv.org/abs/2011.14263 · HTML: https://mlsanity.com/paper/2011.14263/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems](https://mlsanity.com/paper/2403.04007/) (2024)
- [Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks](https://mlsanity.com/paper/2505.11461/) (2025)
- [Model-Based and Data-Driven Hierarchical Control and Topology Co-Design for Robust Networked Systems](https://mlsanity.com/paper/2606.11596/) (2026)

## Abstract (arXiv)

We consider the problem of designing distributed controllers to stabilize a class of networked systems, where each subsystem is dissipative and designs a reinforcement learning based local controller to maximize an individual cumulative reward function. We develop an approach that enforces dissipativity conditions on these local controllers at each subsystem to guarantee stability of the entire networked system. The proposed approach is illustrated on a DC microgrid example, where the objective is maintain voltage stability of the network using local distributed controllers at each generation unit.