Paper ID: 2111.06025

Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control

William Arnold, Tarang Srivastava, Lucas Spangher, Utkarsha Agwan, Costas Spanos

Optimizing prices for energy demand response requires a flexible controller with ability to navigate complex environments. We propose a reinforcement learning controller with surprise minimizing modifications in its architecture. We suggest that surprise minimization can be used to improve learning speed, taking advantage of predictability in peoples' energy usage. Our architecture performs well in a simulation of energy demand response. We propose this modification to improve functionality and save in a large scale experiment.

Submitted: Nov 11, 2021