Demand Response Using Multi-Agent Reinforcement Learning (April 6, 2021)

Reinforcement learning (RL) has gained popularity in the research community as a model-free and adaptive control paradigm for the built environment, especially for building energy control. RL has the potential to enable inexpensive plug-and-play building controllers that can be implemented without necessitating potentially expensive control models and to coordinate multiple buildings for demand response, load shaping, and load shifting. In this presentation, Civil, Architectural and Environmental Engineering Assistant Professor Zoltan Nagy gives an overview of CityLearn, an OpenAI Gym environment to facilitate development of Multi-Agent Reinforcement Learning controllers to study interacting buildings and building-grid interaction.