Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control

Abstract

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.

References in corpus (3)

• A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks
• SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments
• Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure