REFERENCES

1. Ayass T, Coqueiro T, Carvalho T, Jailton J, Araújo J, Francês R. Unmanned aerial vehicle with handover management fuzzy system for 5G networks: challenges and perspectives. Intell Robot 2022;2:20-6.

2. Zhang JD, Yang QM, Shi GQ, Lu Y, Wu Y. UAV cooperative air combat maneuver decision based on multi-agent reinforcement learning. J Syst Eng Electron 2021;6:1421-88.

3. Yang QM, Zhang JD, Shi GQ, Hu JW, Wu Y. Maneuver decision of UAV in short-range air combat based on deep reinforcement learning. IEEE Access 2020;8:363-78.

4. Ruan WY, Duan HB, Deng YM. Autonomous maneuver decisions via transfer learning pigeon-inspired optimization for UCAVs in dogfight engagements. IEEE/CAA J Autom Sinica 2022;9:1639-57.

5. Yang Z, Zhou DY, Piao HY, Zhang K, Kong WR, Pan Q. Evasive maneuver strategy for UCAV in beyond-visual-range air combat based on hierarchical multi-objective evolutionary algorithm. IEEE Access 2020;8:46605-23.

6. Xu GY, Liu Q, Zhang HM. The application of situation function in differential game problem of the air combat. 2018 Chinese Automation Congress (CAC); 2018 Nov 30-Dec 2; Xi'an, China. IEEE; 2019. pp. 1190–5.

7. Başpınar B, Koyuncu E. Differential flatness-based optimal air combat maneuver strategy generation. AIAA Scitech 2019 Forum; 2019 Jan 7-11; San Diego, CA, USA. AIAA; 2019. pp. 1–10.

8. Wang D, Zu W, Chang HX, Zhang J. Research on automatic decision making of UAV based on Plan Goal Graph. 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO); 2016 Dec 3-7; Qingdao, China. IEEE; 2016. pp. 1245–9.

9. Özbek MM, Koyuncu E. Reinforcement learning based air combat maneuver generation; 2022.

10. Zhang YT, Zhang YM, Yu ZQ. Path following control for UAV using deep reinforcement learning approach. Guid Navigat Control 2021;1:2150005.

11. Zhang H, He L, Wang D. Deep reinforcement learning for real-world quadrupedal locomotion: a comprehensive review. Intell Robot 2022;2:275-97.

12. Boin C, Lei L, Yang SX. AVDDPG - Federated reinforcement learning applied to autonomous platoon control. Intell Robot 2022;2:145-67.

13. Li YF, Shi JP, Jiang W, Zhang WG, Lyu YX. Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm. Def Technol 2022;9:1697-714.

14. Li Y, Han W, Wang YG. Deep reinforcement learning with application to air confrontation intelligent decision-making of manned/unmanned aerial vehicle cooperative system. IEEE Access 2020;8:67887-98.

15. Li LT, Zhou ZM, Chai JJ, Liu Z, Zhu YH, Yi JQ. Learning continuous 3-DoF air-to-air close-in combat strategy using proximal policy optimization. 2022 IEEE Conference on Games (CoG); 2022 Aug 21-24; Beijing, China. IEEE; 2022. pp. 616–9.

16. Kang YM, Liu Z, Pu ZQ, Yi JQ, Zu W. Beyond-visual-range tactical game strategy for multiple UAVs. 2019 Chinese Automation Congress (CAC); 2019 Nov 22-24; Hangzhou, China. IEEE; 2019. pp. 5231–6.

17. Ma XT, Xia L, Zhao QC. Air-combat strategy using deep Q-learning. 2018 Chinese Automation Congress (CAC); 2018 Nov 30-Dec 2; Xi'an, China. IEEE; 2019. pp. 3952–7.

18. Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O. Proximal policy optimization algorithms; 2017.

19. Wu JT, Li HY. Deep ensemble reinforcement learning with multiple deep deterministic policy gradient algorithm. Math Probl Eng 2020;2020:1-12.

20. Yuksek B, Demirezen MU, Inalhan G, Tsourdos A. Cooperative planning for an unmanned combat aerial vehicle fleet using reinforcement learning. J Aerosp Inform Syst 2021;18:739-50.

21. Xing JW. RLCodebase: PyTorch codebase for deep reinforcement learning algorithms; 2020. Available from: https://github.com/KarlXing/RLCodebase. [Last accessed on 15 Mar 2023].

22. Chung JY, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling; 2014.

23. Pope AP, Ide JS, Micovic D, et al. Hierarchical reinforcement learning for air-to-air combat; 2021.

24. Sun ZX, Piao HY, Yang Z, et al. Multi-agent hierarchical policy gradient for air combat tactics emergence via self-play. Eng Appl Artif Intel 2021;98:104112.

25. Hu JW, Wang LH, Hu TM, Guo CB, Wang YX. Autonomous maneuver decision making of dual-UAV cooperative air combat based on deep reinforcement learning. Electronics 2022;11:467.

26. Jing XY, Hou MY, Wu GL, Ma ZC, Tao ZX. Research on maneuvering decision algorithm based on improved deep deterministic policy gradient. IEEE Access 2022;10:92426-45.

27. Yang AW, Li ZW, Li B, Xi ZF, Gao CQ. Air combat situation assessment based on dynamic variable weight. Acta Armamentarii 2021;42:1553-63.

Intelligence & Robotics
ISSN 2770-3541 (Online)
Follow Us

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/