About Me
"The powerful play goes on...
And you may contribute a verse."
And you may contribute a verse."
Hi, this is Yue Lin (/ˈjuːeɪ lɪn/, or 林越 in Chinese), and welcome to my personal website. Currently I am a Ph.D. student (data science program) in School of Data Science at The Chinese University of Hong Kong, Shenzhen, fortunately advised by Prof. Baoxiang Wang [Homepage] and Prof. Hongyuan Zha [Google Scholar]. Here, I will irregularly update my experiences, notes, and computational magic spells that interest me.
My current research interest lies in using computational methods to study mechanisms, to address some social dilemma issues in game theory, in which scenarios everyone being self-interested may lead to the detriment of social welfare. [Click here to see details of my research interests]
- The method I use is Multi-Agent Reinforcement Learning (MARL), essentially with the rationality assumption. And I have recently started working with some tools that seem more "human-like", like large language models.
- Specifically I am focusing on the problem of Bayesian persuasion (BP, [my blog]) in economics: a sender with an informational advantage tries to persuade a receiver, who has different motives, to take actions that are beneficial to the sender. My research is on sequential decision-making situations.
- A representative work on this is this [my blog] (NeurIPS), where we proposed a general model-free RL algorithm for multi-agent communication (for the cognoscenti: policy gradient for communication), and expanded the constraints in BP so that mixed-motive communication (even between two agents) in MARL is conceivable.
- During my undergraduate years (specifically, from 2019 to 2021), I dabbled in robotics, understood kinematics, and played a bit with dynamics [my repo].
- I presented a purely simulation-based robotic mechanism design work [my blog] at ICRA. It is about a hybrid leg can transform into various forms (wheel, legs, RHex) to adapt to different terrains, and can even climb ladders.
- Also I have implemented [my blog] a "gimbal" using a hyper-redundant manipulator (purely based on kinematics), allowing it to efficiently reach into barrels.
And how could one endure being a man, if not also for the possibility to create, guess riddles, and redeem accidents? To redeem those who lived in the past and to recreate all "it was" into "thus I willed it" — that alone should I call redemption. — Friedrich Nietzsche, Thus Spoke Zarathustra.
Research Interests
Currently
- Multi-Agent RL: Mixed-Motive Tasks
- Game Theory: Information Design
- Large Language Models
Formerly
- Redundant Manipulator Control
- Robotic Mechanism Design
Education & Experience
Education
- The Chinese University of Hong Kong, Shenzhen
Ph.D. Student in Data Science (2024.8 - Present) - Tiangong University
Bachelor of Engineering in Computer Science and Technology (2018.9 - 2022.6)- School of Computer Science and Technology (2019.9 - 2022.6)
GPA: 3.89 / 4 (92.22 / 100); Rank: 1 / 127
[Certification] - School of Mechanical Engineering (2018.9 - 2019.6)
GPA: 3.90 / 4 (92.00 / 100); Rank: 1 / 60
- School of Computer Science and Technology (2019.9 - 2022.6)
Experience
- The Chinese University of Hong Kong, Shenzhen
Research Assistant in School of Data Science (2022.2 - Present)
Selected Publications
Data Science
- Information Design in Multi-Agent Reinforcement Learning.
Yue Lin, Wenhao Li, Hongyuan Zha, Baoxiang Wang.
Neural Information Processing Systems (NeurIPS) 2023.Poster. This is currently my most representative work.
[Paper] [Code] [Experiments] [Blog en] [Blog cn] [Zhihu cn] [Slides] [Talk en] [Talk RLChina][Click to check the Abstract]
Reinforcement learning (RL) is inspired by the way human infants and animals learn from the environment. The setting is somewhat idealized because, in actual tasks, other agents in the environment have their own goals and behave adaptively to the ego agent. To thrive in those environments, the agent needs to influence other agents so their actions become more helpful and less harmful. Research in computational economics distills two ways to influence others directly: by providing tangible goods (mechanism design) and by providing information (information design). This work investigates information design problems for a group of RL agents. The main challenges are two-fold. One is the information provided will immediately affect the transition of the agent trajectories, which introduces additional non-stationarity. The other is the information can be ignored, so the sender must provide information that the receiver is willing to respect. We formulate the Markov signaling game, and develop the notions of signaling gradient and the extended obedience constraints that address these challenges. Our algorithm is efficient on various mixed-motive tasks and provides further insights into computational economics. Our code is publicly available at https://github.com/YueLin301/InformationDesignMARL.[Click to check the BibTex code]
@article{lin2023information, title={Information design in multi-agent reinforcement learning}, author={Lin, Yue and Li, Wenhao and Zha, Hongyuan and Wang, Baoxiang}, journal={Advances in Neural Information Processing Systems}, volume={36}, pages={25584--25597}, year={2023} }
Robotics
- Innovative Design and Simulation of a Transformable Robot with Flexibility and Versatility, RHex-T3.
Yue Lin, Yujia Tian, Yongjiang Xue, Shujun Han, Huaiyu Zhang, Wenxin Lai, Xuan Xiao.
International Conference on Robotics and Automation (ICRA) 2021.Oral. Delivered a presentation at the Xi’an conference venue.
[Paper] [Blog] [Demo Videos][Click to check the Abstract]
This paper presents a transformable RHex-inspired robot, RHex-T3, with high energy efficiency, excellent flexibility and versatility. By using the innovative 2-DoF transformable structure, RHex-T3 inherits most of RHex’s mobility, and can also switch to other 4 modes for handling various missions. The wheel-mode improves the efficiency of RHex-T3, and the leg-mode helps to generate a smooth locomotion when RHex-T3 is overcoming obstacles. In addition, RHex-T3 can switch to the claw-mode for transportation missions, and even climb ladders by using the hook-mode. The simulation model is conducted based on the mechanical structure, and thus the properties in different modes are verified and analyzed through numerical simulations.[Click to check the BibTex Code]
@inproceedings{lin2021innovative, title={Innovative design and simulation of a transformable robot with flexibility and versatility, RHex-T3}, author={Lin, Yue and Tian, Yujia and Xue, Yongjiang and Han, Shujun and Zhang, Huaiyu and Lai, Wenxin and Xiao, Xuan}, booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)}, pages={6992--6998}, year={2021}, organization={IEEE} } - A snake-inspired path planning algorithm based on reinforcement learning and self-motion for hyper-redundant manipulators.
Yue Lin, Jianming Wang, Xuan Xiao, Ji Qu, Fatao Qin.
International Journal of Advanced Robotic Systems (IJARS) 2022.[Click to check the Abstract]
Redundant manipulators are flexible enough to adapt to complex environments, but their controller is also required to be specific for their extra degrees of freedom. Inspired by the morphology of snakes, we propose a path planning algorithm named Swinging Search and Crawling Control, which allows the snake-like redundant manipulators to explore in complex pipeline environments without collision. The proposed algorithm consists of the Swinging Search and the Crawling Control. In Swinging Search, a collision-free manipulator configuration that of the end-effector in the target point is found by applying reinforcement learning to self-motion, instead of designing joint motion. The self-motion narrows the search space to the null space, and the reinforcement learning makes the algorithm use the information of the environment, instead of blindly searching. Then in Crawling Control, the manipulator is controlled to crawl to the target point like a snake along the collision-free configuration. It only needs to search for a collision-free configuration for the manipulator, instead of searching collision-free configurations throughout the process of path planning. Simulation experiments show that the algorithm can complete path planning tasks of hyper-redundant manipulators in complex environments. The 16 DoFs and 24 DoFs manipulators can achieve 83.3% and 96.7% success rates in the pipe, respectively. In the concentric pipe, the 24 DoFs manipulator has a success rate of 96.1%.[Click to check the BibTex code]
@article{lin2022snake, title={A snake-inspired path planning algorithm based on reinforcement learning and self-motion for hyper-redundant manipulators}, author={Lin, Yue and Wang, Jianming and Xiao, Xuan and Qu, Ji and Qin, Fatao}, journal={International Journal of Advanced Robotic Systems}, volume={19}, number={4}, pages={17298806221110022}, year={2022}, publisher={SAGE Publications Sage UK: London, England} }
Professional Services
Independent Reviewer
- NeurIPS 2024 [6]
- ICLR 2025 [3]
- IMCL 2025 [0]
- TMLR 2025 [0]
Assistant Reviewer (for Advisor)
- AAMAS 2024 [3], 2025 [2]
Numbers in brackets indicate how many manuscripts were reviewed. A “0” indicates that the invitation was accepted, but no review assignment has been made yet. Total: 14.
Teaching
Teaching Assistant
- CSC6021/AIR6001 Artificial Intelligence (2024-25 Term 2). Professor: Chris Ding.
Contact
| Types | ID |
|---|---|
| linyue3h1@gmail.com | |
| R01SVP | |
| GitHub | github.com/YueLin301 |
| Google Scholar | Yue Lin |
| Zhihu | R01SVP |
| Bilibili | 不梦眠 |
This post is licensed under CC BY 4.0 by the author.