| Product-based neural networks for user response prediction Y Qu, H Cai, K Ren, W Zhang, Y Yu, Y Wen, J Wang ICDM 2016, 1149-1154, 2016 | 728 | 2016 |
| Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games Y Wen*, P Peng*, Y Yang, Q Yuan, Z Tang, H Long, J Wang Emergent Communication Workshop, NIPS 2017, 2017 | 564 | 2017 |
| Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning J Grudzien Kuba, R Chen, M Wen, Y Wen, F Sun, J Wang, Y Yang ICLR 2022, 2022 | 185* | 2022 |
| SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving M Zhou, J Luo, J Villela, Y Yang, D Rusu, J Miao, W Zhang, M Alban, ... CoRL 2020 (Best System Paper Award), 2020 | 181* | 2020 |
| Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning Y Wen, Y Yang, R Luo, J Wang, W Pan ICLR 2019, 2018 | 159 | 2018 |
| Multi-Agent Reinforcement Learning is a Sequence Modeling Problem M Wen, JG Kuba, R Lin, W Zhang, Y Wen, J Wang, Y Yang NeurIPS 2022, 2022 | 125 | 2022 |
| Offline pre-trained multi-agent decision transformer: One big sequence model tackles all smac tasks L Meng, M Wen, Y Yang, C Le, X Li, W Zhang, Y Wen, H Zhang, J Wang, ... Machine Intelligence Research, 2023 | 75 | 2023 |
| Multi-Agent Determinantal Q-Learning Y Wen, Y Yang, L Chen, J Wang, K Shao, D Mguni, W Zhang ICML 2020, 2020 | 72* | 2020 |
| Factorized q-learning for large-scale multi-agent systems M Zhou, Y Chen, Y Wen, Y Yang, Y Su, W Zhang, D Zhang, J Wang Proceedings of the first international conference on distributed artificial …, 2019 | 70 | 2019 |
| Learning text representation using recurrent convolutional neural network with highway layers Y Wen, W Zhang, R Luo, J Wang Neu-IR 2016, SIGIR, 2016 | 65 | 2016 |
| Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning Y Wen, Y Yang, J Wang The 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), 2020 | 62 | 2020 |
| Modelling behavioural diversity for learning in open-ended games N Perez-Nieves, Y Yang, O Slumbers, DH Mguni, Y Wen, J Wang International conference on machine learning, 8514-8524, 2021 | 59 | 2021 |
| Towards unifying behavioral and response diversity for open-ended learning in zero-sum games X Liu, H Jia, Y Wen, Y Hu, Y Chen, C Fan, Z Hu, Y Yang Advances in Neural Information Processing Systems 34, 941-952, 2021 | 58* | 2021 |
| Learning in Nonzero-Sum Stochastic Games with Potentials D Mguni, Y Wu, Y Du, Y Yang, Z Wang, M Li, Y Wen, J Jennings, J Wang ICML 2021, 2021 | 54* | 2021 |
| Neural auto-curricula in two-player zero-sum games X Feng, O Slumbers, Z Wan, B Liu, S McAleer, Y Wen, J Wang, Y Yang Advances in Neural Information Processing Systems 34, 3504-3517, 2021 | 46* | 2021 |
| A study of ai population dynamics with million-agent reinforcement learning Y Yang, L Yu, Y Bai, J Wang, W Zhang, Y Wen, Y Yu AAMAS 2018, 2017 | 45* | 2017 |
| A Regularized Opponent Model with Maximum Entropy Objective Y Wen*, Z Tian*, Z Gong, F Punakkath, S Zou, J Wang The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 2019 | 44* | 2019 |
| MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning M Zhou, Z Wan, H Wang, M Wen, R Wu, Y Wen, Y Yang, W Zhang, ... JMLR, 2023 | 40 | 2023 |
| Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems Y Yang, J Luo, Y Wen, O Slumbers, D Graves, HB Ammar, J Wang, ... AAMAS 2021, Blue Sky Track, Best Paper Award, 2021 | 36 | 2021 |
| Alphazero-like tree-search can guide large language model decoding and training X Feng, Z Wan, M Wen, Y Wen, W Zhang, J Wang ICML 2024, 2023 | 23 | 2023 |
Jun WangProfessor, Computer Science, University College London在 cs.ucl.ac.uk 的电子邮件经过验证
