CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor常红星
Degree Grantor中国科学院大学
Place of Conferral北京
Keyword深度强化学习 机动决策 战术决策 空战仿真 多机协同




Other Abstract
With all the countries in the world to develop UAVs and UCAVs, UAV has become the important part of military forces. The intelligent degree of UAV is one of the key factors for determining the level of UAV. Deep learning is one of the most rapid development in the past 10 years, has a successful application in the field of text, speech and image. Especially deep reinforcement learning is proposed, which has a far-reaching influence on the field of artificial intelligence. This paper mainly studies how the deep learning method is applied to UAV air combat, in order to improve the intelligent level of tactics decision-making of UAV air combat . The main work includes the following parts:

1. The deep reinforcement learning is applied to one-to-one air combat tactical maneuver. The definition of UAV air combat environment, including the state of the system, optional maneuver, air combat situation assessment; the reward function according to the energy and angle of the classic tactical tactical design system for learning; deep learning model used in deep reinforcement learning is applied to air combat training, and multi level experiments are taken and get good results.

2. Design air combat simulation system with rich features. The definition of the interface versatility, can achieve a variety of air combat simulation; a large amount of data generated in the air combat simulation and training are collected and screened and long-term persisted; make full use of the stored data to learn the further optimization of the deep neural network.

3. This paper puts forward a multi-UCAV formation combat tactics decision-making method. According to the different UAV attack effect, space is divided into 4 blocks of different threat areas; design the target assignment algorithm for multi-UCAV coorperation; the typical 4 to 2 formation air combat simulation is carried out to verify the effectiveness of the method, raise the level of intelligent multi machine tactical decision.

Subject Area智能信息系统
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
张业胜. 深度强化学习在多机对战战术决策中的应用研究[D]. 北京. 中国科学院大学,2018.
Files in This Item:
File Name/Size DocType Version Access License
深度强化学习在多机对战战术决策中的应用研(6414KB) 暂不开放--Application Full Text
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[张业胜]'s Articles
Baidu academic
Similar articles in Baidu academic
[张业胜]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[张业胜]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.