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- W4320802300 abstract "In this paper, the advantages and disadvantages of two kinds of deep reinforcement learning algorithms DQN and DDPG applying in UAV autonomous decision-making are compared. Firstly, DQN and DDPG models are designed to realize UAV maneuver decision-making in air combat. Secondly, the two models are trained to pursue the target UAV making uniform rectilinear motion respectively. Finally, the confrontation experiments between the two models are carried out. The results show that DDPG has the advantages of action continuity and shorter decision time. But DQN has better decision-making ability under the same model complexity." @default.
- W4320802300 created "2023-02-15" @default.
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- W4320802300 date "2022-08-15" @default.
- W4320802300 modified "2023-10-06" @default.
- W4320802300 title "Autonomous Maneuver Decision of UAV Based on Deep Reinforcement Learning: Comparison of DQN and DDPG" @default.
- W4320802300 cites W2075110355 @default.
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- W4320802300 doi "https://doi.org/10.1109/ccdc55256.2022.10033863" @default.
- W4320802300 hasPublicationYear "2022" @default.
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