Hierarchical Reinforcement Learning Framework in Geographic Coordination for Air Combat Tactical Pursuit

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.

Automated summary

This paper proposes a hierarchical reinforcement learning framework for air combat training to address the non-convergence problem caused by the curse of dimensionality in the state space during air combat tactical pursuit. By using hierarchical reinforcement learning, three-dimensional problems are transformed into two-dimensional problems, resulting in improved training performance compared to other baselines. To further enhance overall learning performance, a meta-learning-based algorithm is established with a corresponding reward function designed to improve the agent's performance in the air combat tactical chase scenario. The results demonstrate that the proposed framework achieves better performance than the baseline approach.