Tengda Li, Gang Wang, Qiang Fu*, Xiangke Guo, Minrui Zhao, Xiangyu Liu
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3499-3522, 2023, DOI:10.32604/cmc.2023.041253
Abstract Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decision-making, such as large computational amount, slow solution speed, and low calculation accuracy, combined with deep reinforcement learning theory, an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed, which uses a double noise mechanism to expand the search range of the action, and introduces a priority experience playback mechanism to effectively achieve data utilization. Finally, the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield. The results of the experiment show that, under the framework of the deep neural… More >
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