Science Score: 39.0%
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○CITATION.cff file
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✓.zenodo.json file
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✓DOI references
Found 1 DOI reference(s) in README -
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○Scientific vocabulary similarity
Low similarity (14.0%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: CLAIR-LAB-TECHNION
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Size: 9.25 MB
Statistics
- Stars: 6
- Watchers: 2
- Forks: 4
- Open Issues: 0
- Releases: 6
Metadata Files
README.md
Multi Taxi Environment
multi-taxi is a highly configurable multi-agent environment, based on gym's
taxi environment, that adheres to the
PettingZoo API. Some configurations include:
1. the number of taxis and passengers in the environment (limited to the size of the map)
2. the domain map itself
3. the environment objective
4. individual taxi configurations:
1. reward function
2. action and observation space
3. passenger and fuel capacity
5. and so much more!
For a quickstart guide and a deeper dive into the environment and its configuraions, please consult our demonstration notebook, also available in colab and nbviewer.
Installation
The easiest way to install multi-taxi is directly from the git repository using pip. Here is how to install the
latest stable version:
shell
pip install "git+https://github.com/CLAIR-LAB-TECHNION/multi-taxi@0.5.0"
You can also download our latest updates by not specifying a tag, like so:
shell
pip install "git+https://github.com/CLAIR-LAB-TECHNION/multi-taxi"
If you wish to install the environment that uses the legacy pettingzoo API, please install version 0.3.0 like so:
shell
pip install "git+https://github.com/CLAIR-LAB-TECHNION/multi-taxi@0.3.0"
If you are seeking the legacy version, which is based on the RLLib
API, please install version 0.0.0 like so:
bash
pip install "git+https://github.com/CLAIR-LAB-TECHNION/multi-taxi@0.0.0"
Acknowledgements
This library is based on MultiTaxiLib by Ofir Abu. The original implementation paper can be found here.
Citation
To cite this repository in academic works or any other purpose, please use the following BibTeX citation:
BibTeX
@article{azranContextualPreplanningReward2024,
title = {Contextual {{Pre-planning}} on {{Reward Machine Abstractions}} for {{Enhanced Transfer}} in {{Deep Reinforcement Learning}}},
author = {Azran, Guy and Danesh, Mohamad H. and Albrecht, Stefano V. and Keren, Sarah},
year = {2024},
month = mar,
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {38},
number = {10},
pages = {10953--10961},
issn = {2374-3468},
doi = {10.1609/aaai.v38i10.28970},
}
Alternatively, we offer a CITATION.cff file with GitHub and Zotero
integration.
Owner
- Name: CLAIR-LAB-TECHNION
- Login: CLAIR-LAB-TECHNION
- Kind: organization
- Repositories: 14
- Profile: https://github.com/CLAIR-LAB-TECHNION
GitHub Events
Total
- Release event: 1
- Issues event: 2
- Watch event: 1
- Delete event: 1
- Issue comment event: 3
- Push event: 8
- Pull request event: 4
- Fork event: 2
Last Year
- Release event: 1
- Issues event: 2
- Watch event: 1
- Delete event: 1
- Issue comment event: 3
- Push event: 8
- Pull request event: 4
- Fork event: 2