FRESCO: Federated Reinforcement Energy System for Cooperative
  Optimization
Nicolas Mauricio Cuadrado Roberto Alejandro Gutierrez Martin Takáč
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.