  Multi-Agent Reinforcement Learning currently focuses on implementations where
all data and training can be centralized to one machine. But what if local
agents are split across multiple tasks, and need to keep data private between
each? We develop the first application of Personalized Federated Hypernetworks
(PFH) to Reinforcement Learning (RL). We then present a novel application of
PFH to few-shot transfer, and demonstrate significant initial increases in
learning. PFH has never been demonstrated beyond supervised learning
benchmarks, so we apply PFH to an important domain: RL price-setting for energy
demand response. We consider a general case across where agents are split
across multiple microgrids, wherein energy consumption data must be kept
private within each microgrid. Together, our work explores how the fields of
personalized federated learning and RL can come together to make learning
efficient across multiple tasks while keeping data secure.

Machine Learning;Artificial Intelligence;Systems and Control;Systems and Control