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，录用/发表论文 1 篇，投出待录用论文0篇。学术成果部分从赵云波教授个人维护的bib文件自动生成，只包含其共同署名的论文/专利（联合培养或代为指导学生可能有未署名论文/专利，不会在此展示），会因为更新不及时而缺失部分论文/专利，如有缺失请及时与老师联系添加更新。
Conference Articles
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Shared Autonomy Based on Human-in-the-loop Reinforcement Learning with Policy Constraints
Ming Li,
Yu Kang,
Yun-Bo Zhao ,
Jin Zhu,
and Shiyi You
In 2022 41st Chin. Control Conf. CCC
2022
[Abs]
[doi]
[pdf]
In shared autonomous systems, humans and agents cooperate to complete tasks. Since reinforcement learning enables agents to train good policies through trial and error without knowing the dynamic model of the environment, it has been well applied in shared autonomous systems. After inferring the target from human inputs, agents trained by RL can accurately act accordingly. However, existing methods of this kind have big problems: the training of reinforcement learning algorithms require lots of exploration, which is time-consuming, lack of security guarantee and likely to cause great losses in the training process. Moreover, most of shared control methods are human-oriented, and do not consider the situation that humans may make wrong actions. In view of the above problems, this paper proposes human-in-the-loop reinforcement learning with policy constraints. In the training process, human prior knowledge is used to constrain the exploration of agents to achieve fast and efficient learning. In the process of testing we incorporate policy constraints in the arbitration to avoid serious consequences caused by human mistakes.