Yuanyang's Academic Page

No.5 Pingcang Lane

No.22 Hankou Road

Nanjing, China

I am pursuing my Ph.D. degree in the Department of Control Science and Intelligence Engineering from Nanjing University, and I am also a member of the Robotics & Reinforcement Learning Control (RLC) Group, under the guidance of Prof. Chunlin Chen. I obtained my Master’s degree in the same department in 2020, under the fortunate mentorship of Prof. Chen, and I am a member of the eXplainable Reinforcement Learning (XRL) group led by Prof. Chen.

My research interests include machine learning, reinforcement learning, interpretability, and robotics. Now, I am working on

Interpretability: I am interested in understanding the behavior of complex machine learning models, such as deep neural networks, and making them more interpretable.
Reinforcement Learning: I am interested in applying reinforcement learning to solve real-world problems, such as robotics and autonomous driving.
Robotics: I am interested in applying machine learning and reinforcement learning to solve robotics problems, such as robot navigation and robot manipulation.
Machine Learning: I am interested in machine learning, especially deep learning and multi-agent reinforcement learning.

news

Jul 16, 2023	An ECAI 2023 paper titled BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization is accepted.
Apr 25, 2023	An ICML paper titled N$A^2$Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning is accepted.
Jan 1, 2023	Invited as Program chair members or reviewers by TNNLS, IJCAI, and ECAI.

latest posts

May 12, 2023	a post with custom blockquotes
Apr 23, 2022	Displaying External Posts on Your al-folio Blog

selected publications

Rule-based reinforcement learning for efficient robot navigation with space reduction

Yuanyang Zhu, Zhi Wang, Chunlin Chen, and 1 more author

IEEE/ASME Transactions on Mechatronics, 2021

Bib HTML

@article{zhu2021rule,
  title = {Rule-based reinforcement learning for efficient robot navigation with space reduction},
  author = {Zhu, Yuanyang and Wang, Zhi and Chen, Chunlin and Dong, Daoyi},
  journal = {IEEE/ASME Transactions on Mechatronics},
  volume = {27},
  number = {2},
  pages = {846--857},
  year = {2021},
  publisher = {IEEE},
  dimensions = {true},
}

MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees

Zichuan Liu, Yuanyang Zhu, Zhi Wang, and 1 more author

arXiv preprint arXiv:2209.07225, 2022

Bib HTML

@article{liu2022mixrts,
  title = {MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees},
  author = {Liu, Zichuan and Zhu, Yuanyang and Wang, Zhi and Chen, Chunlin},
  journal = {arXiv preprint arXiv:2209.07225},
  year = {2022},
  dimensions = {true},
}

NA^2Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning

Zichuan Liu, Yuanyang Zhu, and Chunlin Chen

arXiv preprint arXiv:2304.13383, 2023

Bib HTML

@article{liu2023n,
  title = {NA$^2$Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning},
  author = {Liu, Zichuan and Zhu, Yuanyang and Chen, Chunlin},
  journal = {arXiv preprint arXiv:2304.13383},
  year = {2023},
  dimensions = {true},
}

Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning

Qingpeng Zhao, Yuanyang Zhu, Zichuan Liu, and 2 more authors

arXiv preprint arXiv:2305.07182, 2023

Bib HTML

@article{zhao2023boosting,
  title = {Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning},
  author = {Zhao, Qingpeng and Zhu, Yuanyang and Liu, Zichuan and Wang, Zhi and Chen, Chunlin},
  journal = {arXiv preprint arXiv:2305.07182},
  year = {2023},
}