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Rapid urbanization, increasing integration of distributed renewable energyresources, energy storage, and electric vehicles introduce new challenges forthe power grid. In the US, buildings represent about 70% of the totalelectricity demand and demand response has the potential for reducing peaks ofelectricity by about 20%. Unlocking this potential requires control systemsthat operate on distributed systems, ideally data-driven and model-free. Forthis, reinforcement learning (RL) algorithms have gained increased interest inthe past years. However, research in RL for demand response has been lackingthe level of standardization that propelled the enormous progress in RLresearch in the computer science community. To remedy this, we createdCityLearn, an OpenAI Gym Environment which allows researchers to implement,share, replicate, and compare their implementations of RL for demand response.Here, we discuss this environment and The CityLearn Challenge, a RL competitionwe organized to propel further progress in this field.

CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management