@inproceedings{98b3a99263994b1a8c41f0dd87ef676b,
title = "Deep Reinforcement Learning Algorithm and Simulation Verification Analysis for Automatic Control of Unmanned Vehicles",
abstract = "This study conducted research mainly on the proven applicability of controlling the unmanned vehicle using a deep reinforcement learning algorithm and relative performance improvements. In specific, this study chose the AirSim platform developed by Microsoft as the simulation environment and conducted simulations mainly in the indoor parking lot Unreal 4 environment. In the simulations, the deep reinforcement learning method applied is Deep Q Networks for its effectiveness as well as simplicity. To improve the performance of the trained network, object detection methodology YOLO v3 is applied as the detection algorithm for the unmanned vehicle, and the network is improved using the output of object detection as its input to accelerate the training process. The implementation of the algorithms has efficiently proven the feasibility of using deep reinforcement learning agents for the unmanned vehicle in the project and the implementation of effective object detection.",
keywords = "Deep reinforcement learning, Simulation, Unmanned vehicles, YOLO",
author = "Yonghong Chen and Yuxiang Zhang and Jiaao Chen and Junyu Zhao and Ke Li and Lijing Wang",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 22nd International Conference on Man-Machine-Environment System Engineering, MMESE 2022 ; Conference date: 21-10-2022 Through 23-10-2022",
year = "2023",
doi = "10.1007/978-981-19-4786-5\_39",
language = "英语",
isbn = "9789811947858",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "279--286",
editor = "Shengzhao Long and Dhillon, \{Balbir S.\}",
booktitle = "Man-Machine-Environment System Engineering - Proceedings of the 22nd International Conference on MMESE",
address = "德国",
}