A reinforcement-learning-based framework is proposed for assisting urban planners in the complex task of optimizing the spatial design of urban communities.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Zheng, Y. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00503-5 (2023).
Silver, D. et al. Nature 529, 484–489 (2016).
Mirhoseini, A. et al. Nature 594, 207–212 (2021).
Moreno, C., Allam, Z., Chabaud, D., Gall, C. & Pratolong, F. Smart Cities 4, 93–111 (2021).
Glaeser, E. The 15-minute city is a dead end — cities must be places of opportunity for everyone. LSE (28 May 2021); https://blogs.lse.ac.uk/covid19/2021/05/28/the-15-minute-city-is-a-dead-end-cities-must-be-places-of-opportunity-for-everyone/
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The author declares no competing interests.
Rights and permissions
About this article
Cite this article
Santi, P. AI improves the design of urban communities. Nat Comput Sci 3, 735–736 (2023). https://doi.org/10.1038/s43588-023-00515-1
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-023-00515-1