An intelligent assignment strategy based on deep reinforcement learning is proposed for the multi-target allocation problem in interception. A comprehensive reward function is designed to improve the training efficiency under the concept of marginal reward in training. Meanwhile, to address the problem of poor searching ability induced by the huge solution space, the action masking module is introduced to modify the search strategy of the original deep Q-network (DQN) algorithm. The results show that the improved algorithm can achieve fast and smooth convergence. The trained strategy is generalizable and can generate a satisfactory allocation scheme in a short time.
LiuKXuC. Overview of target assignment algorithm and guidance law for multi-aircraft cooperative interception. Tactical Missile Technol2022; 214(04): 90–97.
2.
ChenZYYuJLDongXW, et al.Three-dimensional cooperative guidance strategy and guidance law for intercepting highly maneuvering target. Chin J Aeronaut2021; 34(5): 485–495.
3.
ShalumovVShimaT. Weapon–target-allocation strategies in multiagent target–missile–defender engagement. J Guid Control Dynam2017; 40(10): 2452–2464.
4.
KlineAAhnerDHillR. The weapon-target assignment problem. Comput Oper Res2019; 105: 226–236.
5.
KongLWangJZZhaoP. Solving the dynamic weapon target assignment problem by an improved multiobjective particle swarm optimization algorithm. Appl Sci2021; 11(19): 9254.
6.
JinTYHeSMLiHY, et al.Evaluation model and exact optimization algorithm in missile–target assignment. J Guid Control Dynam2023; 46(9): 1834–1841.
7.
LuYChenDZ. A new exact algorithm for the weapon-target assignment problem. Omega2021; 98: 102138.
8.
LiWHLyuYXDaiSF, et al.A multi-target consensus-based auction algorithm for distributed target assignment in cooperative beyond-visual-range air combat. Aerospace2022; 9(9): 486.
9.
DavisMTRobbinsMJLundayBJ. Approximate dynamic programming for missile defense interceptor fire control. Eur J Oper Res2017; 259(3): 873–886.
10.
ChangTQKongDPHaoN, et al.Solving the dynamic weapon target assignment problem by an improved artificial bee colony algorithm with heuristic factor initialization. Appl Soft Comput2018; 70: 845–863.
11.
XinBChenJPengZH, et al.An efficient rule-based constructive heuristic to solve dynamic weapon-target assignment problem. IEEE Trans Syst Man Cybern A2010; 41(3): 598–606.
12.
ZhaiSBLiGWWuG, et al.Cooperative task allocation for multi heterogeneous aerial vehicles using particle swarm optimization algorithm and entropy weight method. Appl Soft Comput2023; 148: 110918.
13.
HeSYueSWangG, et al.Target assignment algorithm for joint air defense operation based on spatial crowdsourcing mode. Electronics2022; 11(11): 1779.
14.
GuoDLiangZXJiangP, et al.Weapon-target assignment for multi-to-multi interception with grouping constraint. IEEE Access2019; 7: 34838–34849.
15.
Waxenegger-WilfingGDresiaKDeekenJ, et al.A reinforcement learning approach for transient control of liquid rocket engines. IEEE Trans Aerosp Electron Syst2021; 57(5): 2938–2952.
WangFZhuXPZhouZ, et al.Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments. Chin J Aeronaut2024; 37(3): 237–257.
18.
TaiLPaoloGLiuM. Virtual-to-real deep reinforcement learning: continuous control of mobile robots for mapless navigation. In: Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Vancouver, BC, Canada: IEEE, 2017, pp. 31–36.
19.
LiJXYaoLXuX, et al.Deep reinforcement learning for pedestrian collision avoidance and human-machine cooperative driving. Inf Sci2020; 532: 110–124.
20.
WangXChenWHWuJW, et al.Video captioning via hierarchical reinforcement learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, USA, 18-23 June 2018, pp. 4213–4222.
21.
ZhaoSWWangJCXuHT, et al.Composite observer-based optimal attitude-tracking control with reinforcement learning for hypersonic vehicles. IEEE Trans Cybern2022; 53(2): 913–926.
22.
GongXPChenWCChenZY. Intelligent game strategies in target-missile-defender engagement using curriculum-based deep reinforcement learning. Aerospace2023; 10(2): 133.
23.
XinBWangYPChenJ. An efficient marginal-return-based constructive heuristic to solve the sensor-weapon-target assignment problem. IEEE Trans Syst Man Cybern Syst2018; 49(12): 2536–2547.
24.
SuttonRSBartoAG. Reinforcement learning: an introduction. Cambridge, MA: MIT Press, 2018.
25.
ThrunSSchwartzA. Issues in using function approximation for reinforcement learning. In: Proceedings of the 1993 Connectionist Models Summer School,Hillsdale, NJ: Psychology Press, 1993, pp. 255–263.
