CityLearn
Summary
CityLearn is a Gymnasium environment and dataset schema for reinforcement-learning and control experiments in building energy coordination, load shaping, and demand response.
Official Artifacts
- Official documentation: https://www.citylearn.net/
- Environment documentation: https://www.citylearn.net/overview/environment.html
- Official code: https://github.com/citylearn-project/CityLearn
- Introducing arXiv source: https://arxiv.org/abs/2012.10504
- Dataset metadata snapshot: citylearn-2020
Dataset Shape
CityLearn combines virtual district/building models, flat time-series data files, schema-defined observation/action spaces, and runtime simulation. It can generate trajectories with observations, continuous control inputs, rewards/costs, and next observations.
Role In The Wiki
CityLearn belongs in the non-vision action-conditioned dataset bucket. It is especially useful as an energy-management simulator for testing how models handle exogenous context, building state, storage/device controls, and delayed effects.
Relation To Foundation TSFM Agenda
Use the source-level agenda mapping in citylearn-2020 rather than duplicating verdict rows here.
At the entity level, CityLearn is a clean simulator-backed control environment rather than a passive forecasting dataset. Its main caveat is artifact/version pinning: experiments should record schema, package version, action set, reward/cost function, and source-data provenance.