Abstract
Population growth and increasing the number of vehicles are causing many different economic and environmental problems. One of the crucial ones is finding a parking space. In order to deal with this issue, we can construct new parking lots or optimizing the old ones. In fact, building new parking costs a lot and will destroy nature. Most of the time there is not enough space to build a new one in cities. Thanks to the evolution of IoT, the implementation of smart parking based on IoT is possible. In this paper, We are focusing on an eco-friendly system called Automated Valet Parking which uses hybrid robotic valets in smart parking and helps optimizing parking space usage with Deep Q-Learning which is a reinforcement learning method, In order to achieve high performance. Because reinforcement learning is a goal absorbing algorithm and by well-defining the objective function (The goal), We can achieve optimum policy.
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Shoeibi, N., Shoeibi, N. (2020). Future of Smart Parking: Automated Valet Parking Using Deep Q-Learning. In: Herrera-Viedma, E., Vale, Z., Nielsen, P., Martin Del Rey, A., Casado Vara , R. (eds) Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions. DCAI 2019. Advances in Intelligent Systems and Computing, vol 1004. Springer, Cham. https://doi.org/10.1007/978-3-030-23946-6_20
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