Lihe Li 李立和

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

• [2025.05] SemDiv was accepted to ICML'25! Also congrats to Ziqian and Bohan on the acceptance of Lapse!👏 See you in Vancouver🇨🇦!
• [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!👏

Instead of discovering novel teammates only at the policy level, we utilize LLMs to propose novel coordination behaviors described in natural language, and then transform them into teammate policies, enhancing teammate diversity and interpretability, eventually learning agents with language comprehension ability and stronger collaboration skills.

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.

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.

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.

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.

We addresse the performance degradation of RL policies when state features (e.g., sensor data) evolve unpredictably by proposing Lapse, a method that reuses old policies by combining them with a state reconstruction model for vanished sensors and leverages past policy experience for offline training of new policies.

We propose a data augmentation technique for offline cooperative MARL, utlizing diffusion models to improve the quality of the datasets.

We formulate domain calibration as a cooperative MARL problem to improve efficiency and fidelity.

• 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.

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

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

• 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.