Ying Wen

Assistant Professor

John Hopcroft Center for Computer Science

Shanghai Jiao Tong University

Biography

Ying Wen is now a tenure-track assistant professor in John Hopcroft Center, Shanghai Jiao Tong University. His research interests include deep reinforcement learning, multi-agent systems, game theory and machine learning systems. He pursued his PhD in the Department of Computer Science, University College London. Before that, Ying earned his MRes(Master of Research) with Distinction Honor from University College London in 2016 and B.Eng. with First Class Honor from Queen Mary, University of London and Beijing University of Posts and Tel. in 2015. He was an intern at Huawei, MediaGamma, Amazon and Baidu.

Recent News

[02/23] A white paper about large-scale pre-trained and multi-agent reinforcement learning.

Multi-agent Reinforcement Learning in (Open-ended) Multimodal Environments (Chinese Version)

[04/22] One paper gets accepted in ACL 2022.

Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech.

[04/22] One paper gets accepted in IEEE TWC.

Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications.

[01/22] One paper gets accepted in ICLR 2022.

Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning.

[09/21] Two papers get accepted in NeurIPS 2021.

[06/21] Open sourced MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning

Paper PDF: https://yingwen.io/malib.html
Project Repo: https://github.com/sjtu-marl/malib

[05/21] Two papers get accepted in ICML 2021.

Learning in Nonzero-Sum Stochastic Games with Potentials.

[05/21] Awarded a Grant from the Shanghai Sailing Program, by STCSM.

[01/21] One paper gets accepted in AAMAS 2021.

Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. Best Paper Award, Blue Sky Ideas Track.

[01/21] One paper gets accepted in ICC 2021.

[11/20] One paper gets accepted in CoRL 2020.

SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving. Best System Paper Award.

[06/20] One paper gets accepted in ICML 2020.

Multi-Agent Determinantal Q-Learning.

[05/20] One paper gets accepted in IJCAI 2020.

Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning.

Selected Publications

Quickly discover relevant content by filtering publications.

Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Weinan Zhang, Jun Wang. MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning. Preprint, 2021.

PDF Code Project

Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou Ammar, Jun Wang, Matthew E. Taylor. Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. Blue Sky Track, AAMAS, Best Paper Award, 2021.

Ying Wen, Yaodong Yang, Liheng Chen, Jun Wang, Kun Shao, David Mguni, Weinan Zhang. Multi-Agent Determinantal Q-Learning. ICML, 2020.

Ying Wen, Yaodong Yang, Rui Luo, Jun Wang. Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning. IJCAI, 2020.

Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, Wei Pan. Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning. ICLR, 2019.

Preprint PDF Code Poster

See all publications

Projects

MALib

A Muti-agent Learning Framework.

SMARTS

Scalable Multi-Agent RL Training School.