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 | 264 | 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 | 213 | 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 | 184 | 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 | 120 | 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 | 83 | 2021 |
Multiagent reinforcement learning with unshared value functions Y Hu, Y Gao, B An IEEE transactions on cybernetics 45 (4), 647-662, 2014 | 70 | 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 | 63 | 2020 |
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 | 50 | 2021 |
Accelerating multiagent reinforcement learning by equilibrium transfer Y Hu, Y Gao, B An IEEE transactions on cybernetics 45 (7), 1289-1302, 2014 | 50 | 2014 |
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 | 44 | 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 | 38 | 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 | 37 | 2019 |
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 | 35 | 2022 |
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 |
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 | 27 | 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 | 22 | 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 | 20 | 2021 |
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 | 18 | 2023 |
Euclid: Towards efficient unsupervised reinforcement learning with multi-choice dynamics model Y Yuan, J Hao, F Ni, Y Mu, Y Zheng, Y Hu, J Liu, Y Chen, C Fan arXiv preprint arXiv:2210.00498, 2022 | 13 | 2022 |
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 |