With no training samples, not limited by specific models, and able to take into account the long-term impact of actions and other advantages, deep reinforcement learning methods have gradually received attention in the research of air combat maneuver decision making. In recent years, with the continuous improvement and development of deep learning (DL) theory, the deep reinforcement learning (DRL) algorithm combined with deep learning and reinforcement learning has become a research hotspot in artificial intelligence. Decision making is the core of air combat, and its rationality will determine the final outcome of air combat. Introductionįrom a macro point of view, air combat decision making refers to one party in air combat providing corresponding control instructions to fighter jets after analyzing and judging battlefield information so that it can complete the dominant attack position occupying the enemy. The superior performance of the algorithm is verified by comparison with different algorithms in the test environment, and the effectiveness of the decision method is verified by simulation of air combat tasks with different difficulty and attack modes. Focusing on the problem of insufficient exploration ability of Ornstein–Uhlenbeck (OU) exploration strategy in the deep deterministic policy gradient (DDPG) algorithm, a heuristic DDPG algorithm was proposed by introducing heuristic exploration strategy, and then a UCAV air combat maneuver decision method based on a heuristic DDPG algorithm is proposed. The UCAV platform model of continuous action space was established. In this paper, the UCAV maneuver decision problem in continuous action space is studied based on the deep reinforcement learning strategy optimization method. It has become an inevitable trend in the development of future air combat battlefields that UCAVs complete air combat tasks independently to acquire air superiority. ![]() With the rapid development of unmanned combat aerial vehicle (UCAV)-related technologies, UCAVs are playing an increasingly important role in military operations.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |