A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Optimizing smart city planning: A deep reinforcement learning framework
We introduce a deep reinforcement learning-based approach for smart city planning, designed to determine the optimal timing for constructing various smart city components such as apartments, base stations, and hospitals over a specified development period. Utilizing the Dueling Deep Q-Network (DQN), the proposed method aims to maximize the city’s population while maintaining a predetermined happiness level of residents in the smart city. This optimization is achieved through strategic construction of smart city components, considering that both the total population and happiness levels are influenced by the interplay between housing, communication, transportation, and healthcare infrastructures, as well as the population ratio. Specifically, we present two distinct formulations of the Markov Decision Process (MDP) for smart city planning to illustrate the practicality of applying reinforcement learning across different scenarios.
Optimizing smart city planning: A deep reinforcement learning framework
We introduce a deep reinforcement learning-based approach for smart city planning, designed to determine the optimal timing for constructing various smart city components such as apartments, base stations, and hospitals over a specified development period. Utilizing the Dueling Deep Q-Network (DQN), the proposed method aims to maximize the city’s population while maintaining a predetermined happiness level of residents in the smart city. This optimization is achieved through strategic construction of smart city components, considering that both the total population and happiness levels are influenced by the interplay between housing, communication, transportation, and healthcare infrastructures, as well as the population ratio. Specifically, we present two distinct formulations of the Markov Decision Process (MDP) for smart city planning to illustrate the practicality of applying reinforcement learning across different scenarios.
Optimizing smart city planning: A deep reinforcement learning framework
Junyoung Park (author) / Jiwoo Baek (author) / Yujae Song (author)
2025
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Optimizing the hyper-parameters of deep reinforcement learning for building control
| Springer Verlag | 2025
| DOAJ | 2023
Model-based offline reinforcement learning framework for optimizing tunnel boring machine operation
| DOAJ | 2024
Model-based offline reinforcement learning framework for optimizing tunnel boring machine operation
| Elsevier | 2024