CityLearn
Official reinforcement learning environment for demand response and load shaping.
https://github.com/intelligent-environments-lab/CityLearn
Category: Energy Systems
Sub Category: Load and Demand Forecasting
Keywords from Contributors
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Last synced: about 11 hours ago
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Repository metadata
Official reinforcement learning environment for demand response and load shaping
- Host: GitHub
- URL: https://github.com/intelligent-environments-lab/CityLearn
- Owner: intelligent-environments-lab
- License: mit
- Created: 2019-06-30T02:41:48.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2025-04-02T15:43:19.000Z (25 days ago)
- Last Synced: 2025-04-20T10:42:42.764Z (8 days ago)
- Language: Python
- Homepage:
- Size: 426 MB
- Stars: 509
- Watchers: 18
- Forks: 176
- Open Issues: 6
- Releases: 48
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
README.md
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:
pip install CityLearn
Documentation
Refer to the docs for documentation of the CityLearn API.
Owner metadata
- Name: Intelligent Environments Laboratory
- Login: intelligent-environments-lab
- Email:
- Kind: organization
- Description:
- Website: http://nagy.caee.utexas.edu
- Location: Austin, TX
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/29540963?v=4
- Repositories: 14
- Last ynced at: 2023-03-04T09:39:30.012Z
- Profile URL: https://github.com/intelligent-environments-lab
GitHub Events
Total
- Create event: 8
- Release event: 4
- Issues event: 12
- Watch event: 47
- Delete event: 4
- Issue comment event: 11
- Member event: 1
- Push event: 55
- Pull request event: 10
- Fork event: 6
Last Year
- Create event: 8
- Release event: 4
- Issues event: 12
- Watch event: 47
- Delete event: 4
- Issue comment event: 11
- Member event: 1
- Push event: 55
- Pull request event: 10
- Fork event: 6
Committers metadata
Last synced: 5 days ago
Total Commits: 1,094
Total Committers: 10
Avg Commits per committer: 109.4
Development Distribution Score (DDS): 0.205
Commits in past year: 186
Committers in past year: 4
Avg Commits per committer in past year: 46.5
Development Distribution Score (DDS) in past year: 0.21
| Name | Commits | |
|---|---|---|
| Kingsley Nweye | e****a@y****m | 870 |
| canteli | j****i@u****u | 151 |
| tccf1109 | 1****a | 38 |
| Dipam Chakraborty | d****7@g****m | 11 |
| dipanjan | d****n@q****m | 11 |
| Archytas3435 | 7****5 | 4 |
| Allen Wu | a****o@g****m | 4 |
| dependabot[bot] | 4****] | 3 |
| Callum Tilbury | c****y@i****m | 1 |
| BernardoCabral24 | b****5@g****m | 1 |
Committer domains:
- instadeep.com: 1
- qnulabs.com: 1
- utexas.edu: 1
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 65
Total pull requests: 77
Average time to close issues: 2 months
Average time to close pull requests: 23 days
Total issue authors: 37
Total pull request authors: 13
Average comments per issue: 2.12
Average comments per pull request: 0.13
Merged pull request: 59
Bot issues: 0
Bot pull requests: 9
Past year issues: 10
Past year pull requests: 14
Past year average time to close issues: 15 days
Past year average time to close pull requests: 13 days
Past year issue authors: 8
Past year pull request authors: 3
Past year average comments per issue: 1.9
Past year average comments per pull request: 0.0
Past year merged pull request: 11
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- kingsleynweye (16)
- lijiayi9712 (4)
- BuntBaum (3)
- QasimWani (3)
- Skywuuuu (3)
- HYDesmondLiu (2)
- KandBM (2)
- Zparty (2)
- SHITIANYU-hue (2)
- MatthewD1993 (1)
- lqhdehub (1)
- Hnecent (1)
- Ganesh-mali (1)
- sihuiren (1)
- JoeLan96 (1)
Top Pull Request Authors
- kingsleynweye (53)
- dependabot[bot] (9)
- allenjeffreywu (3)
- calofonseca (2)
- DipG02 (2)
- vbsinha (1)
- johnpap474 (1)
- anjukan (1)
- nikohou (1)
- pkj415 (1)
- callumtilbury (1)
- jiahanxie353 (1)
- ludwigbald (1)
Top Issue Labels
- enhancement (21)
- bug (19)
- fix before v2 publication (8)
- documentation (1)
Top Pull Request Labels
- dependencies (9)
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 1,747 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 47
- Total maintainers: 1
pypi.org: citylearn
An open source Farama Foundation Gymnasium environment for benchmarking distributed energy resource control algorithms to provide energy flexibility in a district of buildings.
- Homepage: https://github.com/intelligent-environments-lab/CityLearn
- Documentation: https://citylearn.readthedocs.io/
- Licenses: MIT License
- Latest release: 2.3.0 (published 4 months ago)
- Last Synced: 2025-04-26T13:41:34.869Z (1 day ago)
- Versions: 47
- Dependent Packages: 0
- Dependent Repositories: 1
- Downloads: 1,747 Last month
-
Rankings:
- Stargazers count: 3.337%
- Forks count: 4.02%
- Downloads: 5.218%
- Dependent packages count: 7.373%
- Average: 8.436%
- Dependent repos count: 22.233%
- Maintainers (1)
Dependencies
- actions/checkout v2 composite
- actions/setup-python v2 composite
- actions/checkout v2 composite
- actions/setup-python v2 composite
- peaceiris/actions-gh-pages v3 composite
Score: 16.013551468676383