Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control

2021/11/11 by William A. Arnold, Arnold, William, Tarang Srivastava +7
Computer Science · Energy · Engineering · #Energy Efficiency and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Smart Grid Energy Management

paper · pdf · doi:10.48550/arxiv.2111.06025

openalex publication_date 2021/11/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Related

• Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs
• Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers
• Go-Explore for Residential Energy Management
• Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture
• Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids