CityLearn is an open source Farama Foundation Gymnasium environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities. A major challenge for RL in demand response is the ability to compare algorithm performance. Thus, CityLearn facilitates and standardizes the evaluation of RL agents such that different algorithms can be easily compared with each other.

CityLearn includes energy models of buildings and distributed energy resources (DER) including air-to-water heat pumps, electric heaters and batteries. A collection of building energy models makes up a virtual district (a.k.a neighborhood or community). In each building, space cooling, space heating and domestic hot water end-use loads may be independently satisfied through air-to-water heat pumps. Alternatively, space heating and domestic hot water loads can be satisfied through electric heaters.

Install latest release in PyPi with pip:

pip install CityLearn

Refer to the docs.

CityLearn UI is a visual dashboard for exploring simulation data generated by the CityLearn framework. It was developed to simplify the analysis of results from smart energy communities, district energy coordination, demand response (among other applications), allowing users to visually inspect building-level components, compare simulation KPIs, and create simulation schemas with ease.

The interface is available in two options:

• Web app: https://citylearnui.netlify.app/ (free hosted version — not recommended for sensitive/personal data)
• Open-source code: https://github.com/Soft-CPS-Research-Group/citylearn-ui

You can check a tutorial at the official CityLearn website, in the CityLearn UI repository README, or at the help tooltip of the oficial webapp.

Compatibility: This version of the UI currently supports CityLearn v2.4.3 simulation data.

Developed by: José, a member of the SoftCPS, Software for Cyber-Physical Systems research group (ISEP, Portugal) in collaboration with the Intelligent Environments Lab, University of Texas at Austin.