About me

课题组学习与研究方向是强化学习的工程应用，面向复杂环境下的无人船、无人车、机器人以及大规模工业自动化系统研发稳健抵抗扰动、学习效率高的强化学习算法。我的主要研究领域是基于概率模型驱动的强化学习实现强化学习的工程应用。

另外我的个人兴趣爱好是编写各种有价值、有趣的web、app应用，我的个人技能包括但不限于学术研究、计算机网络安全、全栈软硬件设计开发、专利撰写、产品设计、商业计划书设计等。

My Works

Wenjun Huang, Yunduan Cui, Huiyun Li and Xinyu Wu (2025): Practical Reinforcement Learning using Time-efficient Model-based Policy Optimization (IEEE Transactions on Automation Science and Engineering (T-ASE), JCR Q1, IF=5.9)( IEEE Xplore )
Wenjun Huang, Yunduan Cui, Huiyun Li and Xinyu Wu (2025): Effective Probabilistic Neural Networks Model for Model-based Reinforcement Learning USV (IEEE Transactions on Automation Science and Engineering (T-ASE), JCR Q1, IF=5.9)( IEEE Xplore );
Wenjun Huang, Yunduan Cui, Huiyun Li and Xinyu Wu (2024): Practical Probabilistic Model-based Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling (IEEE Transactions on Neural Networks and Learning Systems (TNNLS), JCR Q1, IF=10.2)( Github, IEEE Xplore );
Shangde Li, Wenjun Huang, Chenyang Miao, Kun Xu, Yidong Chen, Tianfu Sun, Yunduan Cu (2024): Efficient Robot Manipulation via Reinforcement Learning with Dynamic Movement Primitives-based Policy (Applied Sciences, JCR Q1, IF=2.7);
In progress: 2 Q1 papers are under review;