个人信息
参与实验室科研项目
复杂环境下非完全信息博弈决策的智能基础模型研究
研究课题
针对不确定复杂环境下多群体博弈决策中的瓶颈问题，围绕其非完全信息、高智能、强动态的特点，从智能模型构建、多群体博弈决策理论形成以及人机对抗性能验证与评估等层面开展研究。
学术成果
共撰写/参与撰写专利 0 项，投出/录用/发表论文 1 篇。
Conference Articles
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Strategy Generation Based on DDPG with Prioritized Experience Replay for UCAV
Junsen Lu,
Yun-Bo Zhao ,
Yu Kang,
Yuhui Wang,
and Yimin Deng
In 2022 Int. Conf. Adv. Robot. Mechatron. ICARM
2022
[Abs]
[doi]
[PDF]
Unmanned combat aerial vehicles are becoming essential participants in future air-combat scenarios, while the optimal control strategy remains a great challenge due to the high dynamics of the aerial vehicles themselves as well as the environmental uncertainties in air-combat. Based on a deep deterministic policy gradient algorithm framework, an air combat decision-making strategy is designed and implemented, and further a prioritized experience replay method is proposed for the proposed algorithm to further improve the efficiency in the training process. Simulation experiments show that, at much reduced training cost, the proposed approach achieves superior air combat performance with fast convergence.