In this study, a car transfer planning system for parking lots was designed based on reinforcement learning. The car transfer planning system for parking lots is an intelligent parking management system that is designed by using reinforcement learning techniques. The system features autonomous decision-making, intelligent path planning and efficient resource utilization. And the problem is solved by constructing a Markov decision process and using a dynamic planning-based reinforcement learning algorithm. The system has the advantage of looking to the future and using reinforcement learning to maximize its expected returns. And this is in contrast to manual transfer planning which relies on traditional thinking. In the context of this paper on parking lots, the states of the two locations form a finite set. The system ultimately seeks to find a strategy that is beneficial to the long-term development of the operation. It aims to prioritize strategies that have positive impacts in the future, rather than those that are focused solely on short-term benefits. To evaluate strategies, as its basis the system relies on the expected return of a state from now to the future. This approach allows for a more comprehensive assessment of the potential outcomes and ensures the selection of strategies that align with long-term goals. Experimental results show that the system has high performance and robustness in the area of car transfer planning for parking lots. By using reinforcement learning techniques, parking lot management systems can make autonomous decisions and plan optimal paths to achieve efficient resource utilization and reduce parking time.
Keywords: Markov decision process; Reinforcement learning; automated planning system; dynamic programming.