Deep reinforcement learning based integrated evasion and impact hierarchical intelligent policy of exo-atmospheric vehicles

This is the authors' abstract. We don't add key points for this paper.

Exo-atmospheric vehicles are constrained by limited maneuverability, which leads to the contradiction between evasive maneuver and precision strike. To address the problem of Integrated Evasion and Impact (IEI) decision under multi-constraint conditions, a hierarchical intelligent decision-making method based on Deep Reinforcement Learning (DRL) was proposed. First, an intelligent decision-making framework of “DRL evasion decision” + “impact prediction guidance decision” was established: it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective, and effectively solves the problem of poor decision-making effect caused by the large IEI decision space. Second, to solve the sparse reward problem faced by evasion decision-making, a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed, and the corresponding Markov decision process was designed. A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method. Simulation results show that the proposed model has good performance and low computational resource requirement. The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy, and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer, which has engineering application value.