Deep reinforcement learning for urban modeling: morphogenesis simulation of self-organized settlements
| buir.advisor | Özcan, Burcu Şenyapılı | |
| dc.contributor.author | H'sain, Houssame Eddine | |
| dc.date.accessioned | 2023-09-06T08:28:29Z | |
| dc.date.available | 2023-09-06T08:28:29Z | |
| dc.date.copyright | 2023-07 | |
| dc.date.issued | 2023-07 | |
| dc.date.submitted | 2023-08-31 | |
| dc.department | Department of Architecture | |
| dc.description | Cataloged from PDF version of article. | |
| dc.description | Thesis (Master's): Bilkent University, Department of Architecture, İhsan Doğramacı Bilkent University, 2023. | |
| dc.description | Includes bibliographical references (leaves 78-92). | |
| dc.description.abstract | Self-organized modes of urban growth could result in high-quality urban space and have notable benefits such as providing affordable housing and wider access to economic opportunities within cities. Modeling this non-linear, complex, and dynamic sequential urban aggregation process requires adaptive sequential decision-making. In this study, a deep reinforcement learning (DRL) approach is proposed to automatically learn these adaptive decision policies to generate self-organized settlements that maximize a certain performance objective. A framework to formulate the self-organized settlement morphogenesis problem as single-agent reinforcement learning (RL) environment is presented. This framework is then verified by developing three environments based on two cellular automata urban growth models and training RL agents using the Deep Q-learning (DQN) and Proximal Policy Optimization (PPO) algorithms to learn sequential urban aggregation policies that maximize performance metrics within those environments. The agents consistently learn to sequentially grow the settlements while adapting their morphology to maximize performance, maintain right-of-way, and adapt to topographic constraints. The method proposed in this study can be used not only to model self-organized settlement growth based on preset performance objectives but also could be generalized to solve various single-agent sequential decision-making generative design problems. | |
| dc.description.degree | M.S. | |
| dc.description.statementofresponsibility | by Houssame Eddine H'sain | |
| dc.embargo.release | 2024-02-25 | |
| dc.format.extent | xvi, 101 leaves : color illustrations, charts ; 30 cm. | |
| dc.identifier.itemid | B162458 | |
| dc.identifier.uri | https://hdl.handle.net/11693/113825 | |
| dc.language.iso | English | |
| dc.publisher | Bilkent University | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Self-organized settlements | |
| dc.subject | Urban morphology | |
| dc.subject | Urban modeling and simulation | |
| dc.subject | Machine learning | |
| dc.subject | Deep reinforcement learning | |
| dc.subject | Agent-based modeling | |
| dc.subject | Design control and planning | |
| dc.title | Deep reinforcement learning for urban modeling: morphogenesis simulation of self-organized settlements | |
| dc.title.alternative | Derin pekiştirmeli öğrenme ile kentsel modelleme: kendiliğinden örgütlenen yerleşimlerin morfogenez simülasyonu | |
| dc.type | Thesis |