7 citations · 16 across the 6 of their papers we have counts for
6 papers
Active Reinforcement Learning for Robust Building Control
Doseok Jang, Larry Yan, Lucas Spangher +1
Reinforcement learning (RL) is a powerful tool for optimal control that has found great success in Atari games, the game of Go, robotic control, and building optimization. RL is al…
Machine Learning for Smart and Energy-Efficient Buildings
Hari Prasanna Das, Yu-Wen Lin, Utkarsha Agwan +7
Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countr…
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning
Doseok Jang, Larry Yan, Lucas Spangher +1
Multi-Agent Reinforcement Learning currently focuses on implementations where all data and training can be centralized to one machine. But what if local agents are split across mul…
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control
William Arnold, Tarang Srivastava, Lucas Spangher +2
Optimizing prices for energy demand response requires a flexible controller with ability to navigate complex environments. We propose a reinforcement learning controller with surpr…
Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs
Doseok Jang, Lucas Spangher, Manan Khattar +3
Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community,…
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response
Doseok Jang, Lucas Spangher, Manan Khattar +2
Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community,…