| Mastering complex control in moba games with deep reinforcement learning D Ye, Z Liu, M Sun, B Shi, P Zhao, H Wu, H Yu, S Yang, X Wu, Q Guo, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6672-6679, 2020 | 310 | 2020 |
| Towards playing full moba games with deep reinforcement learning D Ye, G Chen, W Zhang, S Chen, B Yuan, B Liu, J Chen, Z Liu, F Qiu, ... Advances in Neural Information Processing Systems 33, 621-632, 2020 | 178 | 2020 |
| Supervised learning achieves human-level performance in moba games: A case study of honor of kings D Ye, G Chen, P Zhao, F Qiu, B Yuan, W Zhang, S Chen, M Sun, X Li, S Li, ... IEEE Transactions on Neural Networks and Learning Systems 33 (3), 908-918, 2020 | 51 | 2020 |
| Juewu-mc: Playing minecraft with sample-efficient hierarchical reinforcement learning Z Lin, J Li, J Shi, D Ye, Q Fu, W Yang arXiv preprint arXiv:2112.04907, 2021 | 35 | 2021 |
| Which heroes to pick? learning to draft in moba games with neural networks and tree search S Chen, M Zhu, D Ye, W Zhang, Q Fu, W Yang IEEE Transactions on Games 13 (4), 410-421, 2021 | 29 | 2021 |
| Actor-critic policy optimization in a large-scale imperfect-information game H Fu, W Liu, S Wu, Y Wang, T Yang, K Li, J Xing, B Li, B Ma, Q Fu, Y Wei International Conference on Learning Representations, 2021 | 26 | 2021 |
| Minerl diamond 2021 competition: Overview, results, and lessons learned A Kanervisto, S Milani, K Ramanauskas, N Topin, Z Lin, J Li, J Shi, D Ye, ... NeurIPS 2021 Competitions and Demonstrations Track, 13-28, 2022 | 25 | 2022 |
| Mapgo: Model-assisted policy optimization for goal-oriented tasks M Zhu, M Liu, J Shen, Z Zhang, S Chen, W Zhang, D Ye, Y Yu, Q Fu, ... arXiv preprint arXiv:2105.06350, 2021 | 23 | 2021 |
| Honor of kings arena: an environment for generalization in competitive reinforcement learning H Wei, J Chen, X Ji, H Qin, M Deng, S Li, L Wang, W Zhang, Y Yu, L Linc, ... Advances in Neural Information Processing Systems 35, 11881-11892, 2022 | 22 | 2022 |
| More agents is all you need J Li, Q Zhang, Y Yu, Q Fu, D Ye arXiv preprint arXiv:2402.05120, 2024 | 20 | 2024 |
| Rltf: Reinforcement learning from unit test feedback J Liu, Y Zhu, K Xiao, Q Fu, X Han, W Yang, D Ye arXiv preprint arXiv:2307.04349, 2023 | 14 | 2023 |
| Future-conditioned unsupervised pretraining for decision transformer Z Xie, Z Lin, D Ye, Q Fu, Y Wei, S Li International Conference on Machine Learning, 38187-38203, 2023 | 14 | 2023 |
| Quality-similar diversity via population based reinforcement learning S Wu, J Yao, H Fu, Y Tian, C Qian, Y Yang, Q Fu, Y Wei The Eleventh International Conference on Learning Representations, 2023 | 13 | 2023 |
| Boosting offline reinforcement learning with residual generative modeling H Wei, D Ye, Z Liu, H Wu, B Yuan, Q Fu, W Yang, Z Li arXiv preprint arXiv:2106.10411, 2021 | 13 | 2021 |
| Revisiting discrete soft actor-critic H Zhou, Z Lin, J Li, Q Fu, W Yang, D Ye arXiv preprint arXiv:2209.10081, 2022 | 12 | 2022 |
| Learning diverse policies in moba games via macro-goals Y Gao, B Shi, X Du, L Wang, G Chen, Z Lian, F Qiu, G Han, W Wang, D Ye, ... Advances in Neural Information Processing Systems 34, 16171-16182, 2021 | 10 | 2021 |
| Greedy when sure and conservative when uncertain about the opponents H Fu, Y Tian, H Yu, W Liu, S Wu, J Xiong, Y Wen, K Li, J Xing, Q Fu, ... International Conference on Machine Learning, 6829-6848, 2022 | 9 | 2022 |
| Towards effective and interpretable human-agent collaboration in moba games: A communication perspective Y Gao, F Liu, L Wang, Z Lian, W Wang, S Li, X Wang, X Zeng, R Wang, ... arXiv preprint arXiv:2304.11632, 2023 | 8 | 2023 |
| Curriculum-based co-design of morphology and control of voxel-based soft robots Y Wang, S Wu, H Fu, Q Fu, T Zhang, Y Chang, X Wang The Eleventh International Conference on Learning Representations, 2023 | 8 | 2023 |
| Autocfr: Learning to design counterfactual regret minimization algorithms H Xu, K Li, H Fu, Q Fu, J Xing Proceedings of the AAAI Conference on Artificial Intelligence 36 (5), 5244-5251, 2022 | 8 | 2022 |
Deheng YeDirector of AI Applications, Tencent在 e.ntu.edu.sg 的电子邮件经过验证
