1. FRESCO: Federated Reinforcement Energy System for Cooperative Optimization
- Author
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Cuadrado, Nicolas Mauricio, Gutierrez, Roberto Alejandro, and Takáč, Martin
- Subjects
Computer Science - Machine Learning - Abstract
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., Comment: Tiny Paper at ICLR 2023
- Published
- 2024