DRL-urban-planning
A deep reinforcement learning (DRL) based approach for spatial layout of land use and roads in urban communities. (Nature Computational Science)
Optimize land and road layouts in urban planning using AI techniques.
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Free · no card · unsubscribe anytimeA deep reinforcement learning (DRL) based approach for spatial layout of land use and roads in urban communities. (Nature Computational Science)
DRL-urban-planning has 306 stars on GitHub. It has been forked 51 times. DRL-urban-planning is written mainly in Jupyter Notebook. It has been in active development since 2023. DRL-urban-planning is available under the MIT license. Its main topics are graph-neural-networks, reinforcement-learning, spatial-planning, urban-planning.
What is DRL-urban-planning?
A deep reinforcement learning (DRL) based approach for spatial layout of land use and roads in urban communities. (Nature Computational Science)
Is DRL-urban-planning open source?
DRL-urban-planning is an open-source project. It is released under the MIT license.
Is DRL-urban-planning free?
Yes. DRL-urban-planning is free and open source — you can use, modify and self-host it.
What license does DRL-urban-planning use?
DRL-urban-planning is available under the MIT license.
What language is DRL-urban-planning written in?
DRL-urban-planning is written mainly in Jupyter Notebook.
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