Faculty of Education, Arts, Science and Technology
Dr Jin is currently a Lecturer in Computer Science at the University of Northampton. Before taking up this post, she was a Research Fellow at the Data Science Group at the University of Warwick.
Dr Jin’s research interest lies in the general areas of artificial intelligence and machine learning, with particular emphasis on deep reinforcement learning, multi-agent reinforcement learning, game theory, autonomous coordination and control, collective intelligence, AI safety, and AI for social good.
[1] Yue Jin, Shuangqing Wei, and Giovanni Montana. Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing. Machine Learning, 2025, 114, no. 190
[2] Charles A Hepburn, Yue Jin, and Giovanni Montana. State-Constrained Offline Reinforcement Learning. Transactions on Machine Learning Research (TMLR), 2025.
[3] Ting Zhu, Yue Jin, and Giovanni Montana. Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning. Transactions on Machine Learning Research (TMLR), 2024.
[4] Mianchu Wang, Yue Jin, and Giovanni Montana. Goal-conditioned offline reinforcement learning through state space partitioning. Machine Learning, 2024, pp. 1-31.
[5] Yunbo Qiu, Yue Jin, Lebin Yu, Jian Wang, Yu Wang, and Xudong Zhang. Improving sample efficiency of multiagent reinforcement learning with nonexpert policy for flocking control. IEEE Internet of Things Journal, 2023,
pp.14014-14027.
[6] Yue Jin, Shuangqing Wei, Jian Yuan, and Xudong Zhang. Hierarchical and Stable Multiagent Reinforcement Learning for Cooperative Navigation Control. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021, 34, no. 1 (2021): 90-103
[7] Yue Jin, and Giovanni Montana. Partial Action Replacement: Tackling Distribution Shift in Offline MARL. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2025.
[8] Mianchu Wang, Yue Jin, and Giovanni Montana. Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement Learning. International Conference on Learning Representations (ICLR), 2025.
[9] Yunbo Qiu, Yue Jin, Lebin Yu, Jian Wang, and Xudong Zhang. Safe Multi-Agent Reinforcement Learning via Dynamic Shielding. In Proceedings of IEEE Conference on Artificial Intelligence (CAI), 2024, pp. 1254-1257.
[10] Yunbo Qiu, Yue Jin, Lebin Yu, Jian Wang, and Xudong Zhang. Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), June 2023, pp. 1–5.
[11] Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, and Tie-Yan Liu. Supervised Off-Policy Ranking. In Proceedings of International Conference on Machine Learning (ICML), Jul 2022, pp.10323-10339.
[12] Yue Jin, Shuangqing Wei, Jian Yuan, and Xudong Zhang. Learning to Advise and Learning from Advice in Cooperative Multiagent Reinforcement Learning. In Proceedings of International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), May 2022, pp.1645-1647.
[13] Yunbo Qiu, Yue Jin, Jian Wang, and Xudong Zhang. Sub-Optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2022, pp. 2709-2714.
[14] Yunbo Qiu, Yuzhu Zhan, Yue Jin, Jian Wang, and Xudong Zhang. Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control. In Proceedings of IEEE 96th Vehicular Technology Conference: VTC2022-Fall, 2022.
[15] Qiongxiao Liu, Ben Li, Yue Jin, Zhemin Huang, and Guohua Xu. Autonomous Underwater Vehicle Path Planning with Hybrid Policy Based Policy-Constrained Deep Reinforcement Learning. In Proceedings of International Ocean and Polar Engineering Conference, 2022, pp.1022-1029.
[16] Yue Jin, Shuangqing Wei, Jian Yuan, and Xudong Zhang. Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement Learning. In Proceedings of IEEE International Conference on Autonomous Systems (ICAS), Aug 2021, pp.1-5.
[17] Yue Jin, Shuangqing Wei, Jian Yuan, Xudong Zhang, and Chao Wang. Stabilizing Multi-Agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020, pp. 3547–3551.
[18] Yue Jin, Yaodong Zhang, Jian Yuan, and Xudong Zhang. Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, pp. 2897–2901.