Hello, welcome

I’m a Ph.D. student in Computer Science at ETH Zurich, advised by Prof. Niao He. Before that, I was a Ph.D. student in Computer Science at University of Illinois at Urbana–Champaign (UIUC), advised by Prof. Nan Jiang. I completed my B.E. in Computer Science at Beihang University.
My research primarily focuses on reinforcement learning (RL) and, more broadly, sequential decision-making under uncertainty. I work on understanding the fundamental mathematical principles underlying the problems and leveraging theoretical insights to develop efficient and practical algorithms. I’m particularly interested in bridging the gap between theory and practice, designing algorithms that come with theoretical supports and demonstrate empirical performance.
My previous research spans a broad spectrum of topics including:
- Reinforcement Learning from Human Feedback (RLHF): Developing methods to align AI with human preferences.
- Multi-Agent Reinforcement Learning (MARL): Understanding learning efficiency in multi-agent systems.
- Offline Reinforcement Learning: Advancing learning algorithms in offline setting.
Contacts: Google Scholar | LinkedIn | Github | jiawei.huang [at] inf [dot] ethz [dot] ch
Research Highlights
Reinforcement Learning from Human Feedback
Sample efficiency is crucial in online RLHF. While previous works focus on strategic exploration for sample-efficient learning, we study the benefits by transfer learning.MARL and Game Theory
Learning equilibrium policy in large-population systems is challenging in general. Our ICML 2024 paper studies a class of large-population called Mean-Field Games (MFGs). Due to its special symmetric structure, we show that learning in MFGs is actually not much harder than single-agent RL.Others
Early in my Ph.D., I explored various topics in single-agent online/offline RL. Motivated by the practical policy switching constraints, our ICLR 2022 paper introduces the deployment-efficient setup, and develops efficient algorithms that match our established lower bounds.- ICLR 2022
(Spotlight)Towards Deployment-Efficient Reinforcement Learning: Lower Bound and OptimalityInternational Conference on Learning Representations, 2022