Planning and Design of Elderly Care Space Combining PER and Dueling DQN

With the continuous development of the aging phenomenon in society, people's attention to the planning of elderly care spaces is increasing. Currently, many scholars have used various spatial planning models to plan and design elderly care spaces. However, the resource utilization rate and comfort of the elderly care spaces designed by these models are low, and the models still need to be optimized. This study first integrates the Prioritized Experience Replay mechanism with the Dueling deep Q-network algorithm, and constructs a spatial planning model based on the fused algorithm, to use this model to plan elderly care spaces reasonably. The study first conducts comparative experiments on the fusion algorithm, and the outcomes indicate that the fusion algorithm has the best prediction performance, with a minimum prediction error rate of only 0.9% and a prediction speed of up to 8.7bps. In addition, the denoising effect of the algorithm is the best, and the performance of the algorithm is much higher than that of the comparative algorithm. Further analysis of the spatial planning model based on this algorithm shows that the average time required for elderly care space planning is only 1.3 seconds, and the comfort level of the planned elderly care space reaches 98.7%, the resource utilization rate reaches 89.7%, and the planned elderly care space can raise the living standard of the elderly by 67.7%. From the above information, the spatial planning model raised in the study can validly enhance the resource utilization and comfort of elderly care spaces, and raise the living standard of the elderly.

@article{Wang2025,
  title     = {Planning and Design of Elderly Care Space Combining PER and Dueling DQN},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Di Wang and Hui Ma and Yu Chen},
  doi       = {10.14569/IJACSA.2025.0160250},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160250}
}