CityLearn
Official reinforcement learning environment for demand response and load shaping
Science Score: 36.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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✓Committers with academic emails
1 of 12 committers (8.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.1%) to scientific vocabulary
Keywords from Contributors
Repository
Official reinforcement learning environment for demand response and load shaping
Basic Info
Statistics
- Stars: 539
- Watchers: 19
- Forks: 190
- Open Issues: 4
- Releases: 52
Metadata Files
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:
console
pip install CityLearn
Documentation
Refer to the docs.
Owner
- Name: Intelligent Environments Laboratory
- Login: intelligent-environments-lab
- Kind: organization
- Location: Austin, TX
- Website: http://nagy.caee.utexas.edu
- Repositories: 14
- Profile: https://github.com/intelligent-environments-lab
GitHub Events
Total
- Create event: 10
- Release event: 8
- Issues event: 20
- Watch event: 67
- Delete event: 4
- Issue comment event: 18
- Member event: 2
- Push event: 73
- Pull request event: 16
- Fork event: 19
Last Year
- Create event: 10
- Release event: 8
- Issues event: 20
- Watch event: 67
- Delete event: 4
- Issue comment event: 18
- Member event: 2
- Push event: 73
- Pull request event: 16
- Fork event: 19
Committers
Last synced: 10 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Kingsley Nweye | e****a@y****m | 893 |
| canteli | j****i@u****u | 151 |
| tccf1109 | 1****a | 40 |
| Rui Pina | r****a@m****m | 36 |
| Rui Pina | 1****8@i****t | 28 |
| 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 |
| BernardoCabral24 | b****5@g****m | 2 |
| Callum Tilbury | c****y@i****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 65
- Total pull requests: 83
- Average time to close issues: 2 months
- Average time to close pull requests: 21 days
- Total issue authors: 39
- Total pull request authors: 15
- Average comments per issue: 2.0
- Average comments per pull request: 0.13
- Merged pull requests: 58
- Bot issues: 0
- Bot pull requests: 9
Past Year
- Issues: 9
- Pull requests: 18
- Average time to close issues: 15 days
- Average time to close pull requests: about 22 hours
- Issue authors: 9
- Pull request authors: 4
- Average comments per issue: 1.11
- Average comments per pull request: 0.06
- Merged pull requests: 8
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- kingsleynweye (15)
- lijiayi9712 (4)
- QasimWani (3)
- Skywuuuu (3)
- HYDesmondLiu (2)
- KandBM (2)
- Zparty (2)
- SHITIANYU-hue (2)
- MatthewD1993 (1)
- lqhdehub (1)
- Hnecent (1)
- Ganesh-mali (1)
- JoeLan96 (1)
- sihuiren (1)
- AmalNamm (1)
Pull Request Authors
- kingsleynweye (61)
- dependabot[bot] (10)
- RuiRioPina (8)
- calofonseca (4)
- allenjeffreywu (3)
- nikohou (2)
- DipG02 (2)
- johnpap474 (1)
- pkj415 (1)
- callumtilbury (1)
- LorenzoBonanni (1)
- ludwigbald (1)
- vbsinha (1)
- anjukan (1)
- jiahanxie353 (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 3
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Total downloads:
- pypi 1,195 last-month
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Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 1
(may contain duplicates) - Total versions: 109
- Total maintainers: 1
proxy.golang.org: github.com/intelligent-environments-lab/citylearn
- Documentation: https://pkg.go.dev/github.com/intelligent-environments-lab/citylearn#section-documentation
- License: mit
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Latest release: v2.4.2+incompatible
published 11 months ago
Rankings
proxy.golang.org: github.com/intelligent-environments-lab/CityLearn
- Documentation: https://pkg.go.dev/github.com/intelligent-environments-lab/CityLearn#section-documentation
- License: mit
-
Latest release: v2.4.2+incompatible
published 11 months ago
Rankings
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/
- License: MIT License
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Latest release: 2.4.2
published 11 months ago
Rankings
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