Oliinyk, V.Danyliuk, Y.2025-05-262025-05-262025Oliinyk, V. Autonomous car parking Model for different types of parking lots using deep reinforcement learning / V. Oliinyk, Y. Danyliuk // Адаптивні системи автоматичного управління : міжвідомчий науково-технічний збірник. – 2025. – № 1 (46). – С. 237-246. – Бібліогр.: 18 назв.https://ela.kpi.ua/handle/123456789/73939This article explores the simulation of automated parking in a virtual environment with various types of parking lots. The objective of this research is to develop an intelligent model for autonomous parking that achieves high efficiency under simulated conditions across a broad range of common parking lot types. We use a deep reinforcement learning approach using the Proximal Policy Optimization (PPO) algorithm, complemented by Behavioural Cloning and Generative Adversarial Imitation Learning. Our fine-tuned model achieves state-of-the-art parking accuracy, ranging from 96.3% to 99.34%, depending on the type of parking lot. The developed simulation environment, based on the Unity game engine and the MLAgents plugin, enables high-quality visualization, simulation, and modelling capabilities, making it valuable for both educational and research purposes.enautomatic parkingself-driving cardeep reinforcement learningProximal Policy OptimizationML-AgentsUnityvirtual environmentArticleС. 237-246https://doi.org/10.20535/1560-8956.46.2025.323821004.942