ADAPTIVE RESOURCE ALLOCATION IN SMART CITIES USING REINFORCEMENT LEARNING

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Smart cities require efficient and adaptive resource allocation mechanisms to manage dynamic urban environments. Traditional methods are often static and fail to respond to real-time changes in demand. This paper presents a reinforcement learning (RL)-based framework for dynamic resource allocation in smart cities. The system models resource allocation as a sequential decision-making process, where an RL agent learns optimal allocation strategies through interaction with the environment. The proposed approach improves resource utilization, reduces operational costs, and enhances service quality. Experimental results demonstrate that the RL-based system outperforms traditional methods in terms of efficiency and adaptability.

References

1. R. Sutton and A. Barto, Reinforcement Learning: An Introduction, MIT Press, 2018.
2. Y. Zhang et al., “Reinforcement learning based dynamic resource allocation in wireless networks,” IEEE, 2019.
3. K. G. Oikonomou et al., “Deep reinforcement learning for traffic signal control,” IEEE, 2020.
4. H. Ye et al., “Deep reinforcement learning for V2V communications,” IEEE, 2019.
5. A. Talari et al., “Smart cities based on IoT,” IEEE, 2017.
6. V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015.
7. R. S. Sutton, D. A. McAllester, S. P. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” in Proc. NIPS, 2000, pp. 1057–1063.
8. X. Li, X. Wang, and V. Leung, “Application of deep reinforcement learning in smart grid: A review,” IEEE Access, vol. 8, pp. 1–15, 2020.
9. H. Mao, M. Alizadeh, I. Menache, and S. Kandula, “Resource management with deep reinforcement learning,” in Proc. ACM HotNets, 2016, pp. 50–56.
10. Y. Chen, L. Li, and B. Vucetic, “Deep reinforcement learning for resource allocation in wireless networks,” IEEE Trans. Wireless Communications, vol. 18, no. 8, pp. 1–12, 2019..

Keywords

Reinforcement Learning, Smart Cities, Resource Allocation, Dynamic Systems, Optimization, Artificial Intelligence.