The threat of North Korea's long-range firepower is recognized as a typical asymmetric threat, and South Korea is prioritizing the development of a Korean-style missile defense system to defend against it. To address this, previous research modeled North Korean long-range artillery attacks as a Markov Decision Process (MDP) and used Approximate Dynamic Programming as an algorithm for missile defense, but due to its limitations, there is an intention to apply deep reinforcement learning techniques that incorporate deep learning. In this paper, we aim to develop a missile defense system algorithm by applying a modified DQN with multi-agent-based deep reinforcement learning techniques. Through this, we have researched to ensure an efficient missile defense system can be implemented considering the style of attacks in recent wars, such as how effectively it can respond to enemy missile attacks, and have proven that the results learned through deep reinforcement learning show superior outcomes.

1. 서 론
2. 배경이론
    2.1 심층강화학습 연구
    2.2 미사일 방어체계와 심층강화학습의 접목 이유
3. 방법론
    3.1 문제정의
    3.2 MDP 모형
    3.3 심층강화학습을 통한 시뮬레이션
4. 시뮬레이션 및 결과 분석
    4.1 시뮬레이션 시나리오
    4.2 시뮬레이션 결과
5. 결 론
Acknowledgement
References

• Min Gook Kim(Department of Industrial Engineering, Hannam University) | 김민국 (한남대학교 산업공학과)
• Dong Wook Hong(Hanwha Systems) | 홍동욱 (한화시스템(주))
• Bong Wan Choi(Department of Industrial Engineering, Hannam University) | 최봉완 (한남대학교 산업공학과)
• Ji Hoon Kyung(Department of Industrial Engineering, Hannam University) | 경지훈 (한남대학교 산업공학과) Corresponding author