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The CityLearn Challenge 2023

Zoltan Nagy · Kingsley Nweye · Sharada Mohanty · Ruchi Choudhary · Max Langtry · Gregor Henze · Jan Drgona · Sourav Dey · Alfonso Capozzoli · Mohamed Ouf



Reinforcement learning has gained popularity as a model-free and adaptive controller for the built-environment in demand-response applications. However, a lack of standardization on previous research has made it difficult to compare different RL algorithms with each other. Also, it is unclear how much effort is required in solving each specific problem in the building domain and how well a trained RL agent will scale up to new environments. The CityLearn Challenge 2023 provides an avenue to address these problems by leveraging CityLearn, an OpenAI Gym Environment for the implementation of RL agents for demand response. The challenge utilizes a novel dataset based on the US end use load profile database. Participants are to develop energy management agents for battery charge and discharge control in each building with a goal of minimizing electricity demand from the grid, electricity bill and greenhouse gas emissions. We provide a baseline RBC agent for the evaluation of the RL agents performance and rank the participants' according to their solution's ability to outperform the baseline.

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