Acknowledgement
이 논문은 2023년 정부의 재원으로 수행된 연구 결과임.
References
- Z. Wang, H. Li, H. Wu and Z. Wu, "Improving Maneuver Strategy in Air Combat by Alternate Freeze Games with a Deep Reinforcement Learning Algorithm," Hindawi Mathematical Problems in Engineering, 2023.
- H. S. Inc., "Heron Systems at DARPA Alpha Dogfight Trials," (Sept. 25, 2020). Accessed: Dec. 06, 2022.,[Online Video]. Available: https://www.youtube.com/watch?v=lldE5XFTA88.
- J. Oh, C. Kim, S. Ro, W. C. Choi and Y. Kim, "Air-to-air BFM Engagement Simulator for AI Engagement Model," in Proc. Korea Inst. Mil. Sci. Technol. Conf., pp. 1753-1754. 2022.
- B. Vlahov, E. Squires, L. Strickland, and C. Pippin, "On Developing a UAV Pursuit-evasion Policy Using Reinforcement Learning," in Proc. IEEE Int. Conf. Mach. Learn. Appl. (ICMLA), pp. 859-864, 2018.
- J. Bae, H. Jung, S. Kim, S. Kim and Y. Kim, "Deep Reinforcement Learning-Based Air-to-Air Combat Maneuver Generation in a Realistic Environment," in IEEE Access, Vol. 11, pp. 26427-26440, 2023. https://doi.org/10.1109/ACCESS.2023.3257849
- M. Wiering and M. Van Otterlo, "Reinforcement Learning", Adaptation, Learning and Optimization, Vol. 12, p. 3, 2012.
- T. Haarnoja, A. Zhou, K. Hartikainen and G. Tucker, "Soft Actor-Critic Algorithms and Applications," 2019, arxiv:1812.05905v2.
- S. Hochreiter and J. Schmidhuber, "Long Short-term Memory," Neural Comput., Vol. 9, No. 8, pp. 1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
- R. Portelas, C. Romac, K. Hofmann, "Automatic Curriculum Learning for Deep RL : A Short Survey," IJCAI, 2021.
- J. Berndt, "Jsbsim: An Open Source Flight Dynamics Model in c++," in Modeling and Simulation Technologies Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2004.
- A. Pope, J. Jaime, D. Mi'covi'c, H. Diaz, D. Rosenbluth, L. Ritholtz, J. Twedt, T. Waler, K. Alcedo, and D. Javorsek, "Hierarchical Reinforcement Learning for Air-to-air Combat," 2021 International Conference on Unmanned Aircraft Systems(ICUAS), pp. 275-284, 2021.