Research on auxiliary decision-making for sea striking of naval aviation based on deep reinforcement learning

Minjie Wu; Dawei Yin

doi:10.1117/12.3000933

9 August 2023 Research on auxiliary decision-making for sea striking of naval aviation based on deep reinforcement learning

Minjie Wu, Dawei Yin

Author Affiliations +

Proceedings Volume 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023); 1278210 (2023) https://doi.org/10.1117/12.3000933
Event: Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The situation of the future naval battlefield will become more and more complex, and it will become a trend to develop various military auxiliary decision-making systems based on artificial intelligence and big data technology. This paper sorts out the key technologies of the auxiliary decision-making system based on deep reinforcement learning. On this basis, it proposes the construction method of the naval aviation sea-striking agent model, and completes the construction of the training framework with the combat deduction system as the environment. Finally, it summarizes and prospects some of future work.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Minjie Wu and Dawei Yin "Research on auxiliary decision-making for sea striking of naval aviation based on deep reinforcement learning", Proc. SPIE 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 1278210 (9 August 2023); https://doi.org/10.1117/12.3000933

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available