Multi-agent Reinforcement Learning Method for Wargame Simulation Based on Suboptimal Demonstration Guidance

Authors

• Zicong Zhou, College of Systems Engineering, National University of Denfense Technology, Changsha 410073, China
• Junjie Zeng, College of Systems Engineering, National University of Denfense Technology, Changsha 410073, China
• Yue Hu, College of Systems Engineering, National University of Denfense Technology, Changsha 410073, China
• Zhengqiu Zhu, College of Systems Engineering, National University of Denfense Technology, Changsha 410073, China
• Quanjun Yin, College of Systems Engineering, National University of Denfense Technology, Changsha 410073, China

Abstract

To address issues such as fixed behavior patterns and insufficient adaptability in complex adversarial environments exhibited by traditional wargame agent decision-making models, this paper proposes a multi-agent reinforcement learning method based on suboptimal demonstrations (MARLSD). The proposed method integrates reward relabeling with a self-imitation learning mechanism, effectively improving the training efficiency of multi-agent reinforcement learning algorithms in environments with large state-action spaces and sparse rewards, even when only a small number of suboptimal demonstrations are available, while encouraging agents to explore better strategies. Experimental results show that, compared with baselines such as QMIX and MAGAIL, MARLSD significantly improves performance and training efficiency, adapts to various value-decomposition multi-agent reinforcement learning algorithms, and achieves strong results using only a small number of suboptimal demonstration trajectories.

Recommended Citation

Zhou, Zicong; Zeng, Junjie; Hu, Yue; Zhu, Zhengqiu; and Yin, Quanjun (2026) "Multi-agent Reinforcement Learning Method for Wargame Simulation Based on Suboptimal Demonstration Guidance," Journal of System Simulation: Vol. 38: Iss. 5, Article 10.
DOI: 10.16182/j.issn1004731x.joss.25-0743
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss5/10

First Page

1277

Last Page

1289

CLC

TP391.9

Recommended Citation

Zhou Zicong, Zeng Junjie, Hu Yue, et al. Multi-agent Reinforcement Learning Method for Wargame Simulation Based on Suboptimal Demonstration Guidance[J]. Journal of System Simulation, 2026, 38(5): 1277-1289.

Corresponding Author

Zeng Junjie

DOI

10.16182/j.issn1004731x.joss.25-0743