Multi-agent Cooperative Combat Simulation in Naval Battlefield with Reinforcement Learning

Authors

Ding Shi, School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;Follow
Xuefeng Yan, School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;
Lina Gong, School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;
Jingxuan Zhang, School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;
Donghai Guan, School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;
Mingqiang Wei, School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;Follow

Abstract

Abstract: Due to the rapidly-changed situations of future naval battlefields, it is urgent to realize the high-quality combat simulation in naval battlefields based on artificial intelligence to comprehensively optimize and improve the combat effectiveness of our army and defeat the enemy. The collaboration of combat units is the key point and how to realize the balanced decision-making among multiple agents is the first task. Based on decoupling priority experience replay mechanism and attention mechanism, a multi-agent reinforcement learning-based cooperative combat simulation (MARL-CCSA) network is proposed. Based on the expert experience, a multi-scale reward function is designed, on which a naval battlefield combat simulation environment is constructed. The proposed multi-scale reward function could speedthe convergence of multiple agents. The feasibility and practicability of MARL-CCSA is verified by the simulation experiment and the comparison with the other methods.

Recommended Citation

Shi, Ding; Yan, Xuefeng; Gong, Lina; Zhang, Jingxuan; Guan, Donghai; and Wei, Mingqiang (2023) "Multi-agent Cooperative Combat Simulation in Naval Battlefield with Reinforcement Learning," Journal of System Simulation: Vol. 35: Iss. 4, Article 9.
DOI: 10.16182/j.issn1004731x.joss.21-1321
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss4/9

First Page

786

Revised Date

2022-03-01

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-1321

Last Page

796

CLC

TP391.9

Recommended Citation

Ding Shi, Xuefeng Yan, Lina Gong, Jingxuan Zhang, Donghai Guan, Mingqiang Wei. Multi-agent Cooperative Combat Simulation in Naval Battlefield with Reinforcement Learning[J]. Journal of System Simulation, 2023, 35(4): 786-796.

Corresponding Author

Mingqiang Wei,mqwei@nuaa.edu.cn

DOI

10.16182/j.issn1004731x.joss.21-1321