Multi agent cooperative electronic countermeasure method based on reinforcement learning

DOI:	10.11809/bqzbgcxb2024.07.001
Keywords:	collaborative decision making; reinforcement learning; policy gradient; electronic countermeasure simulation
Abstract:	Traditional electronic warfare is gradually evolving into intelligent electronic warfare that integrates artificial intelligence technology. In view of the problem that multi agent reinforcement learning algorithm is not easy to converge in complex and high dimensional state action space, a multi agent dual adversarial strategy gradient algorithm based on preferential experience playback is proposed. The algorithm introduces a preferential experience playback mechanism, and presents a counter Critic network and a dual Critic network to balance the relationship between action and value and to reduce the uncertainty of a single Critic network. The simulation results show that compared with other reinforcement learning algorithms, the PerMaD4 algorithm has better convergence effect and the task completion degree is increased by 8.9% in the same simulation scene.
Issue:	Vol. 45 No. 7 (2024)
Published:	2024-07-26
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