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ORCIDJiahui Li 0003
Person information
- affiliation: Zhejiang University, Hangzhou, China
Other persons with the same name
- Jiahui Li — disambiguation page
- Jiahui Li 0001
![[0000-0002-3953-7345]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-3953-7345")
— Tsinghua University, Department of Electronic Engineering, Beijing, China - Jiahui Li 0002 — Jilin University, Changchun, China
- Jiahui Li 0004 — Northeast Petroleum University, Daqing, China
- Jiahui Li 0005 — SenseTime Research, Beijing, China
- Jiahui Li 0006 — Wireless Technology Lab, Huawei, Shenzhen, China
- Jiahui Li 0007 — Zhejiang University: Haining, Zhejiang, China (and 1 more)
- Jiahui Li 0008 — Sun Yat-sen University, Guangzhou, China
- Jiahui Li 0009 — Alibaba Group, China
- Jiahui Li 0010 — Tsinghua University, Department of Computer Science and Technology, Beijing, China
- Jiahui Li 0011 — Harbin Engineering University, China
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2020 – today
- 2024
[c6]Jiahui Li, Hanlin Zhang, Fengda Zhang, Tai-Wei Chang, Kun Kuang, Long Chen, Jun Zhou:
Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning. EMNLP 2024: 10122-10140- 2023
- [c5]Jiahui Li, Kun Kuang, Baoxiang Wang, Xingchen Li, Fei Wu, Jun Xiao, Long Chen:
Two Heads are Better Than One: A Simple Exploration Framework for Efficient Multi-Agent Reinforcement Learning. NeurIPS 2023 - 2022
- [c4]Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao:
Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning. ICML 2022: 12843-12856 - [c3]Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu:
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? KDD 2022: 1183-1191 - [i3]Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu:
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? CoRR abs/2206.11054 (2022) - 2021
- [c2]Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao:
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning. KDD 2021: 934-942 - [c1]Jiahui Li, Kun Kuang, Lin Li, Long Chen, Songyang Zhang, Jian Shao, Jun Xiao:
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation. ACM Multimedia 2021: 3664-3672 - [i2]Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao:
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning. CoRR abs/2106.00285 (2021) - [i1]Jiahui Li, Kun Kuang, Lin Li, Long Chen, Songyang Zhang, Jian Shao, Jun Xiao:
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation. CoRR abs/2109.01369 (2021)
Coauthor Index
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