  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.
