Personalized Federated Hypernetworks for Multi-Task Reinforcement Learning in Microgrid Energy Demand Response

As sensors pervade the built environment, they have fueled the advance of data-driven models that promise greater efficiency for microgrid management. However, this has raised concerns over data privacy and data ownership. The paradigm of federated learning has emerged in supervised learning to address these issues, but work on federated RL is relatively rare, and focuses on training global models that do not take into account the heterogeneity of data from different microgrids. 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 avoiding the need for centralized data storage.