Hierarchical Reinforcement Learning Framework in Geographic Coordination for Air Combat Tactical Pursuit
Ruihai Chen, Hao Li, Guanwei Yan, Haojie Peng, Qian Zhang
Abstract
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.