Xinran Li

xinran.li@connect.ust.hk HKUST, Hong Kong SAR, China

Hi there! I am a third-year Ph.D. student at Hong Kong University of Science and Technology (HKUST), advised by Prof. Jun Zhang. I am also fortunate to work closely with Prof. Ling Pan.

Prior to that, I received my Bachelor’s degree in Electronic and Information Engineering from Beijing Institute of Technology (BIT) in 2020. As part of my undergraduate experience, I spent time at the Australian National University (ANU) working on my honor thesis under the supervision of Prof. Salman Durrani in 2020. In 2021, I interned at the Department of Open Source Algorithm System at SenseTime, mentored by Dr. Wenwei Zhang and led by Dr. Kai Chen.

My research centers on decision-making problems, with a particular focus on multi-agent systems. I primarily leverage reinforcement learning and embodied AI as core methodologies to tackle these challenges.

You can find my CV here (Last updated: November 2024).

I am always open to discussions and collaborations! Feel free to reach out to me via email or wechat (lxr-rrr).

News

Sep 26, 2024	Our paper, Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning, has been accepted by NeurIPS 2024!
Aug 05, 2024	I have joined the embodied AI team led by Dr. Chenjia Bai as a research intern at TeleAI.

Selected Publications

Context-aware Communication for Multi-agent Reinforcement Learning

Xinran Li, and Jun Zhang

In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

Bib PDF Code

@inproceedings{li2024CACOM,
  title = {Context-aware Communication for Multi-agent Reinforcement Learning},
  author = {Li, Xinran and Zhang, Jun},
  booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems},
  pages = {1156--1164},
  year = {2024},
}

Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning

Xinran Li, Ling Pan, and Jun Zhang

In Advances in Neural Information Processing Systems, 2024

Bib PDF Video Code

@inproceedings{li2024kaleidoscope,
  title = {Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning},
  author = {Li, Xinran and Pan, Ling and Zhang, Jun},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2024},
}

Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning

Xinran Li, Zifan Liu, Shibo Chen, and Jun Zhang

In Proceedings of the 41st International Conference on Machine Learning, 2024

Bib PDF Video Code

@inproceedings{liindividual,
  title = {Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning},
  author = {Li, Xinran and Liu, Zifan and Chen, Shibo and Zhang, Jun},
  booktitle = {Proceedings of the 41st International Conference on Machine Learning},
  year = {2024},
}