About me
My name is pronounced as shin-yoo. I am a Ph.D. student at the State Key Laboratory of Software Development Environment (SKLSDE) and School of Computer Science and Engineering (SCSE) , Beihang University, Beijing, China, supervised by Prof. Wenjun Wu and Rongye Shi.
Research
My research focuses on controlling multi-robot systems through multi-agent reinforcement learning (MARL). To address the practical training challenges of reinforcement learning (RL), I integrate domain expertise with RL to improve sample efficiency and convergence. This strategy not only mitigates the practical training constraints of RL but also expands the applicability of MARL in multi-robot operations. In addition, I am interested in Imitation Learning, Embodied AI based on Large Language Models (LLM), and Robust MARL.
Recent News
[2024.7] One co-authored paper on robust MARL accepted by ECAI 2024.
[2024.7] One co-authored paper on consensus MARL accepted by IROS 2024.
[2024.01] One first-authored paper on Multi-robot accepted by ICRA 2024.
[2023.12] One co-authored paper on robust MARL accepted by ICLR 2024.
[2023.12] One first-authored paper on physical-informed MARL accepted by AAAI 2024.
[2023.12] One co-authored paper on reinforcement learning accepted by ICDE 2024.
[2023.07] One first-authored paper on physical-informed MARL accepted by ECAI 2023.
[2023.06] One co-authored paper on efficient MARL accepted by ICSI 2023.
Publications
- Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning
Xin Yu, Rongye Shi, Yongkai Tian,Simin Li,Shuhao Liao, Wenjun Wu
AAAI 2024 (CCF A) $\vert$ pdf $\vert$ Project page - ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning
Xin Yu, Rongye Shi, Pu Feng, Yongkai Tian, Jie Luo, Wenjun Wu
ECAI 2023 (CCF B) $\vert$ pdf $\vert$ Project page - Swarm inverse reinforcement learning for biological systems
Xin Yu, Pu Feng, Yongkai Tian, Wenjun Wu
BIBM 2021 (CCF B) $\vert$ pdf AdaptAUG: Adaptive Data Augmentation Framework for Multi-Agent Reinforcement Learning
Xin Yu,Yongkai Tian, Li Wang, Pu Feng, Wenjun Wu, Rongye Shi
ICRA 2024 (CCF B) $\vert$ pdf $\vert$ Project page- GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
Tianhao Peng , Wenjun Wu, Haitao Yuan, Zhifeng Bao , Zhao Pengrui , Xin Yu , Xuetao Lin , Yu Liang , Yanjun Pu
ICDE 2024 (CCF A) $\vert$ pdf - Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game
Simin Li, Jun Guo, Jingqiao Xiu, Xu Yu, Jiakai Wang, Aishan Liu, Yaodong Yang, Xianglong Liu
ICLR 2024 - MACT: Multi-agent Collision Avoidance with Continuous Transition Reinforcement Learning via Mixup
Pu Feng, Xin Yu, Junkang Liang, Wenjun Wu, Yongkai Tian.
ICSI $\vert$ pdf - Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
Simin Li, Jun Guo, Jingqiao Xiu, Pu Feng, Xin Yu, Jiakai Wang, Aishan Liu, Wenjun Wu, Xianglong Liu
arXiv preprint (Submitted to TCYB)
Work in Progress
Exploiting Spatio-Temporal Symmetry for Multi-Agent Reinforcement Learning
Xin Yu, Rongye Shi, Yongkai Tian, Li Wang, Tianhao Peng, Simin Li, Pu Feng, Wenjun Wu
(Submitted to IJCAI 2024)Decentralized Multi-Agent Reinforcement Learning with Equivariant Graph Neural Network
Xin Yu, Yongkai Tian, Yandong Wang, Shuhao Liao, Pu Feng, Tianhao Peng, Rongye Shi, Wenjun Wu
(Submitted to IROS 2024)Integrating Permutation Invariance into Inverse Reinforcement Learning: A New Approach to Collective Behavior Analysis
Xin Yu, Yongkai Tian, Haoyuan Li, Tianhao Peng, Rongye Shi, Wenjun Wu
(Submitted to Briefings in Bioinformatics)Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
Simin Li, Jun Guo, Jingqiao Xiu, Pu Feng, Xin Yu, Jiakai Wang, Aishan Liu, Wenjun Wu, Xianglong Liu
(Submitted to IEEE TCYB)Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
Pu Feng*, Junkang Liang, Size Wang, Xin Yu, Rongye Shi, Wenjun Wu
(Submitted to IROS 2024)
Book
I am one of the writers of the book ”AIDevOps: Principles and Practices of Development, Operation and Maintenance of Intelligent Microservices”.