# FRESCO: Federated Reinforcement Energy System for Cooperative Optimization

arXiv: https://arxiv.org/abs/2403.18444 · HTML: https://mlsanity.com/paper/2403.18444/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 3 references that are in the map.
- First posted on arXiv 27 Mar 2024.

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

- [Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL](https://mlsanity.com/paper/2412.20946/) (2024)

## Abstract (arXiv)

The rise in renewable energy is creating new dynamics in the energy grid that promise to create a cleaner and more participative energy grid, where technology plays a crucial part in making the required flexibility to achieve the vision of the next-generation grid. This work presents FRESCO, a framework that aims to ease the implementation of energy markets using a hierarchical control architecture of reinforcement learning agents trained using federated learning. The core concept we are proving is that having greedy agents subject to changing conditions from a higher level agent creates a cooperative setup that will allow for fulfilling all the individual objectives. This paper presents a general overview of the framework, the current progress, and some insights we obtained from the recent results.