Lihe Li

Lihe Li 李立和

How to pronounce my name?

Lihe -> lee-huh.
Li -> lee.
You can just call me Lee.

Hi there, thanks for visiting my website! I am a M.Sc. student (Sep. 2023 - Now) at the School of Artificial Intelligence at Nanjing University, where I am fortunate to be advised by Prof. Yang Yu and affiliated with the LAMDA Group led by Prof. Zhi-Hua Zhou. Specifically, I am a member of the LAMDA-RL Group, which focuses on reinforcement learning research. Prior to that, I obtained my bachelor's degree at the same school and university in June 2023.

Unity makes strength. Currently my research interest is Reinforcement Learning (RL), especially in Multi-agent Reinforcement Learning (MARL) that enables agents efficiently, robustly and safely coordinate with other agents🤖 and even humans👨‍👩‍👧‍👦.

Please feel free to drop me an Email for any form of communication or collaboration!

Email: lilh [at] lamda [dot] nju [dot] edu [dot] cn /

lilhzq76 [at] gmail [dot] com

CV / Google Scholar / Semantic Scholar / DBLP / Github / Twitter

Just a reminder, I am the guy on the left.

News

[2025.01] MADiTS was accepted to ICLR'25!
[2024.09] Madoc was accepted to NeurIPS'24! Huge congrats to Tao!👏
[2024.05] DASaR was accepted to ECML PKDD'24! Huge congrats to Ruiqi!👏
[2024.04] MACPro was accepted to TNNLS!
[2024.04] CORe3 was accepted to IJCAI'24!👏 Look forward to seeing you in Jeju🇰🇷!
[2023.12] COSTA was accepted to AAMAS'24! See you in Auckland🇳🇿!
[2023.12] We released A Survey of Progress on Cooperative MARL in Open Environment. Welcome to follow our work!
[2023.12] Macop was accepted to DAI'23🇸🇬 and won the Best Paper Award!👏

Selected Publications [ Full List / Google Scholar ]

	Continual Multi-Objective Reinforcement Learning via Reward Model Rehearsal Lihe Li, Ruotong Chen, Ziqian Zhang, Zhichao Wu, Yi-Chen Li, Cong Guan, Yang Yu, Lei Yuan The 33rd International Joint Conference on Artificial Intelligence (IJCAI), 2024 pdf / link / code / talk / poster / bibtex `@inproceedings{core3, title = {Continual Multi-Objective Reinforcement Learning via Reward Model Rehearsal}, author = {Lihe Li and Ruotong Chen and Ziqian Zhang and Zhichao Wu and Yi-Chen Li and Cong Guan and Yang Yu and Lei Yuan}, booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence}, pages = {4434--4442}, year = {2024} }` We study the problem of multi-objective reinforcement learning (MORL) with continually evolving learning objectives, and propose CORe3 to enable the MORL agent rapidly learn new objectives and avoid catastrophic forgetting about old objectives lacking reward signals.
	Learning to Coordinate with Anyone Lei Yuan, Lihe Li, Ziqian Zhang, Feng Chen, Tianyi Zhang, Cong Guan, Yang Yu, Zhi-Hua Zhou Proceedings of the Fifth International Conference on Distributed Artificial Intelligence (DAI), Best Paper Award , 2023 pdf / link / English talk / Chinese talk / bibtex `@inproceedings{macop, title = {Learning to Coordinate with Anyone}, author = {Lei Yuan and Lihe Li and Ziqian Zhang and Feng Chen and Tianyi Zhang and Cong Guan and Yang Yu and Zhi-Hua Zhou}, booktitle = {Proceedings of the Fifth International Conference on Distributed Artificial Intelligence}, year = {2023} }` We propose Multi-agent Compatible Policy Learning (MACOP), where we adopt an agent-centered teammate generation process that gradually and efficiently generates diverse teammates covering the teammate policy space, and we use continual learning to train the ego agents to coordinate with them and acquire strong coordination ability.
	Multi-agent Continual Coordination via Progressive Task Contextualization Lei Yuan, Lihe Li, Ziqian Zhang, Fuxiang Zhang, Cong Guan, Yang Yu IEEE Transactions on Neural Networks and Learning Systems (TNNLS) pdf / link / code / poster / bibtex `@article{macpro, title = {Multi-agent Continual Coordination via Progressive Task Contextualization}, author = {Lei Yuan and Lihe Li and Ziqian Zhang and Fuxiang Zhang and Cong Guan and Yang Yu}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, volume = {36}, number = {4}, pages = {6326-6340}, year = {2025} }` We formulate the continual coordination framework and propose MACPro to enable agents to continually coordinate with each other when the dynamic of the training task and the multi-agent system itself changes over time.
	A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment Lei Yuan, Ziqian Zhang, Lihe Li, Cong Guan, Yang Yu Science China Information Sciences (SCIS) pdf in English / pdf in Chinese / link / bibtex `@article{survey, title = {A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment}, author = {Lei Yuan and Ziqian Zhang and Lihe Li and Cong Guan and Yang Yu}, journal = {Science China Information Sciences (SCIS)}, year = {2023} }` We review multi-agent cooperation from closed environment to open environment settings, and provide prospects for future development and research directions of cooperative MARL in open environments.
	Efficient Multi-agent Offline Coordination via Diffusion-based Trajectory Stitching Lei Yuan, Yuqi Bian, Lihe Li, Ziqian Zhang, Cong Guan, Yang Yu The 13th International Conference on Learning Representations (ICLR), 2025 pdf / link / bibtex `@inproceedings{madits, title = {Efficient Multi-agent Offline Coordination via Diffusion-based Trajectory Stitching}, author = {Lei Yuan and Yuqi Bian and Lihe Li and Ziqian Zhang and Cong Guan and Yang Yu}, booktitle = {The Thirteenth International Conference on Learning Representations}, year = {2025} }` We propose a data augmentation technique for offline cooperative MARL, utlizing diffusion models to improve the quality of the datasets.
	Multi-Agent Domain Calibration with a Handful of Offline Data Tao Jiang, Lei Yuan, Lihe Li, Cong Guan, Zongzhang Zhang, Yang Yu Advances in Neural Information Processing Systems 38 (NeurIPS), 2024 pdf / link / code / bibtex `@inproceedings{madoc, title = {Multi-Agent Domain Calibration with a Handful of Offline Data}, author = {Tao Jiang and Lei Yuan and Lihe Li and Cong Guan and Zongzhang Zhang and Yang Yu}, booktitle = {Advances in Neural Information Processing Systems 38}, pages = {69607--69636}, year = {2024} }` We formulate domain calibration as a cooperative MARL problem to improve efficiency and fidelity.

Education

	Nanjing University 2023.09 - present M.Sc. in Computer Science and Technology Advisor: Prof. Yang Yu
	Nanjing University 2019.08 - 2023.07 B.E. in Artificial Intelligence Advisor: Prof. Yang Yu

Awards & Honors

National Scholarship, 2024. [link]
Best Paper Award of The Fifth Distributed Artificial Intelligence Conference (DAI), 2023.
Outstanding Graduate & Bachelor's Thesis of Nanjing University, 2023.
The Egret Scholarship, 2022.
The People's Scholarship, 2021, 2020.

Service

Reviewer: NeurIPS (2025), COLM (2025), ICML (2025), ICLR (2025), IEEE TETCI.

Teaching Assistant

Introduction to Artificial Intelligence (with Prof. Yang Yu; for undergraduate students, Fall, 2024).

Miscellaneous

I have the fortune to work with brilliant people during my research journey and I am truly grateful for their guidance and help!
My Chinese name, 李立和 (Li Lihe), can be pronounced as /liː ˈliː hɜː/ in Mandarin or /lei ˈlʌb wɔː/ in Cantonese. 李 is one of the most common surnames in China, 立 means "stand" or "establish", and 和 means "harmony" and "peace".
I enjoy singing and I am a Tenor of the Nanjing University Chorus🎼. I was even awarded as an Outstanding Person in the first year😆!
I enjoy working out, like going to the gym💪 and playing basketball🏀. I've been crazy about Prince lately.
This website template was stolen from my good friend Zhaoxuan. Appreciate that🫡.

Template courtesy: Jon Barron.