Chen Ruihai, Li Hao, Yan Guanwei, Peng Haojie, Zhang Qian
School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China.
Chengdu Aircraft Design and Research Institute, Chengdu 610041, China.
Entropy (Basel). 2023 Oct 1;25(10):1409. doi: 10.3390/e25101409.
This paper proposes an air combat training framework based on hierarchical reinforcement learning to address the problem of non-convergence in training due to the curse of dimensionality caused by the large state space during air combat tactical pursuit. Using hierarchical reinforcement learning, three-dimensional problems can be transformed into two-dimensional problems, improving training performance compared to other baselines. To further improve the overall learning performance, a meta-learning-based algorithm is established, and the corresponding reward function is designed to further improve the performance of the agent in the air combat tactical chase scenario. The results show that the proposed framework can achieve better performance than the baseline approach.
本文提出了一种基于分层强化学习的空战训练框架，以解决空战战术追击过程中由于状态空间过大导致的维度灾难所引起的训练不收敛问题。利用分层强化学习，三维问题可以转化为二维问题，与其他基线方法相比，提高了训练性能。为了进一步提高整体学习性能，建立了一种基于元学习的算法，并设计了相应的奖励函数，以进一步提高智能体在空中战战术追击场景中的性能。结果表明，所提出的框架能够比基线方法取得更好的性能。
