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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Official reinforcement learning environment for demand response and load shaping
| Date | Stars |
|---|---|
| 2026-07-31 | 626 |
| 2026-08-01 | 626 |
| 2026-08-02 | 626 |
| 2026-08-05 | 627 |
| 2026-08-06 | 627 |
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# CityLearn 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.  ## Environment Overview 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.  ## Installation Install latest release in PyPi with `pip`: ```console pip install CityLearn ``` Optional dependency for PV autosizing (`PySAM`): ```console pip install "CityLearn[pysam]" ``` ## Documentation Refer to the [docs](https://intelligent-environments-lab.github.io/CityLearn/). ## CityLearn UI 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](https://intelligent-environments-lab.github.io/CityLearn/ui.html), in the CityLearn UI repository [README](https://github.com/Soft-CPS-Research-Group/citylearn-ui), or at the help [tooltip of the oficial webapp](https://citylearn-ui.netlify.app/admin/help). **Compatibility:** This version of the UI currently supports CityLearn v2.5.0 simulation data. The KPI export consumed by the UI is generated from `evaluate_v2()` by default, and `evaluate()` remains available only for legacy workflows. **Developed by:** José, a member of the [SoftCPS](https://www2.isep.ipp.pt/softcps/), Software for Cyber-Physical Systems research group (ISEP, Portugal) in collaboration with the Intelligent Environments Lab, University of Texas at Austin.
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e71a5d64926d7c0a, desc:reinforcement learning