Self-learning-based Multiple Spacecraft Evasion Decision Making Simulation Under Sparse Reward Condition

Authors

Zhao Yu, School of Astronautics, Harbin Institute of Technology, Harbin 150001, China;
Jifeng Guo, School of Astronautics, Harbin Institute of Technology, Harbin 150001, China;
Yan Peng, School of Astronautics, Harbin Institute of Technology, Harbin 150001, China;
Chengchao Bai, School of Astronautics, Harbin Institute of Technology, Harbin 150001, China;

Abstract

Abstract: In order to improve the ability of spacecraft formation to evade multiple interceptors, aiming at the low success rate of traditional procedural maneuver evasion, a multi-agent cooperative autonomous decision-making algorithm, which is based on deep reinforcement learning method, is proposed. Based on the actor-critic architecture, a multi-agent reinforcement learning algorithm is designed, in which a weighted linear fitting method is proposed to solve the reliability allocation problem of the self-learning system. To solve the sparse reward problem in task scenario, a sparse reward reinforcement learning method based on inverse value method is proposed. According to the task scenario, the space multi-agent countermeasure simulation system is established, and the correctness and effectiveness of the proposed algorithm are verified.

Recommended Citation

Yu, Zhao; Guo, Jifeng; Peng, Yan; and Bai, Chengchao (2021) "Self-learning-based Multiple Spacecraft Evasion Decision Making Simulation Under Sparse Reward Condition," Journal of System Simulation: Vol. 33: Iss. 8, Article 2.
DOI: 10.16182/j.issn1004731x.joss.21-0432
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss8/2

First Page

1766

Revised Date

2021-06-05

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0432

Last Page

1774

CLC

TP391.9

Recommended Citation

Zhao Yu, Guo Jifeng, Yan Peng, Bai Chengchao. Self-learning-based Multiple Spacecraft Evasion Decision Making Simulation Under Sparse Reward Condition[J]. Journal of System Simulation, 2021, 33(8): 1766-1774.

DOI

10.16182/j.issn1004731x.joss.21-0432