| Multi-agent game abstraction via graph attention neural network Y Liu, W Wang, Y Hu, J Hao, X Chen, Y Gao Proceedings of the AAAI conference on artificial intelligence 34 (05), 7211-7218, 2020 | 237 | 2020 |
| Reinforcement learning to rank in e-commerce search engine: Formalization, analysis, and application Y Hu, Q Da, A Zeng, Y Yu, Y Xu Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 204 | 2018 |
| Learning to utilize shaping rewards: A new approach of reward shaping Y Hu, W Wang, H Jia, Y Wang, Y Chen, J Hao, F Wu, C Fan Advances in Neural Information Processing Systems 33, 15931-15941, 2020 | 161 | 2020 |
| From few to more: Large-scale dynamic multiagent curriculum learning W Wang, T Yang, Y Liu, J Hao, X Hao, Y Hu, Y Chen, C Fan, Y Gao Proceedings of the AAAI conference on artificial intelligence 34 (05), 7293-7300, 2020 | 111 | 2020 |
| Episodic multi-agent reinforcement learning with curiosity-driven exploration L Zheng, J Chen, J Wang, J He, Y Hu, Y Chen, C Fan, Y Gao, C Zhang Advances in Neural Information Processing Systems 34, 3757-3769, 2021 | 71 | 2021 |
| Multiagent reinforcement learning with unshared value functions Y Hu, Y Gao, B An IEEE transactions on cybernetics 45 (4), 647-662, 2014 | 68 | 2014 |
| Q-value path decomposition for deep multiagent reinforcement learning Y Yang, J Hao, G Chen, H Tang, Y Chen, Y Hu, C Fan, Z Wei International Conference on Machine Learning, 10706-10715, 2020 | 60 | 2020 |
| Accelerating multiagent reinforcement learning by equilibrium transfer Y Hu, Y Gao, B An IEEE transactions on cybernetics 45 (7), 1289-1302, 2014 | 48 | 2014 |
| 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 | 45 | 2021 |
| Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns. Y Liu, Y Hu, Y Gao, Y Chen, C Fan IJCAI, 457-463, 2019 | 42 | 2019 |
| Efficient deep reinforcement learning via adaptive policy transfer T Yang, J Hao, Z Meng, Z Zhang, Y Hu, Y Cheng, C Fan, W Wang, W Liu, ... arXiv preprint arXiv:2002.08037, 2020 | 35 | 2020 |
| Action semantics network: Considering the effects of actions in multiagent systems W Wang, T Yang, Y Liu, J Hao, X Hao, Y Hu, Y Chen, C Fan, Y Gao arXiv preprint arXiv:1907.11461, 2019 | 32 | 2019 |
| Learning in Multi-agent Systems with Sparse Interactions by Knowledge Transfer and Game Abstraction. Y Hu, Y Gao, B An AAMAS, 753-761, 2015 | 31 | 2015 |
| Individual reward assisted multi-agent reinforcement learning L Wang, Y Zhang, Y Hu, W Wang, C Zhang, Y Gao, J Hao, T Lv, C Fan International Conference on Machine Learning, 23417-23432, 2022 | 25 | 2022 |
| An efficient transfer learning framework for multiagent reinforcement learning T Yang, W Wang, H Tang, J Hao, Z Meng, H Mao, D Li, W Liu, Y Chen, ... Advances in neural information processing systems 34, 17037-17048, 2021 | 24 | 2021 |
| Fever basketball: A complex, flexible, and asynchronized sports game environment for multi-agent reinforcement learning H Jia, Y Hu, Y Chen, C Ren, T Lv, C Fan, C Zhang arXiv preprint arXiv:2012.03204, 2020 | 20 | 2020 |
| Unifying behavioral and response diversity for open-ended learning in zero-sum games X Liu, H Jia, Y Wen, Y Yang, Y Hu, Y Chen, C Fan, Z Hu arXiv preprint arXiv:2106.04958, 2021 | 17 | 2021 |
| Efficient Deep Reinforcement Learning through Policy Transfer. T Yang, J Hao, Z Meng, Z Zhang, Y Hu, Y Chen, C Fan, W Wang, Z Wang, ... AAMAS, 2053-2055, 2020 | 13 | 2020 |
| Aligndiff: Aligning diverse human preferences via behavior-customisable diffusion model Z Dong, Y Yuan, J Hao, F Ni, Y Mu, Y Zheng, Y Hu, T Lv, C Fan, Z Hu arXiv preprint arXiv:2310.02054, 2023 | 11 | 2023 |
| Mastering basketball with deep reinforcement learning: An integrated curriculum training approach H Jia, C Ren, Y Hu, Y Chen, T Lv, C Fan, H Tang, J Hao Proceedings of the 19th International Conference on Autonomous Agents and …, 2020 | 11 | 2020 |
Yingfeng Chen(陈赢峰)Fuxi AI Lab in Netease在 mail.ustc.edu.cn 的电子邮件经过验证
