| Celebrating diversity in shared multi-agent reinforcement learning C Li, T Wang, C Wu, Q Zhao, J Yang, C Zhang Advances in Neural Information Processing Systems 34, 3991-4002, 2021 | 128 | 2021 |
| Seihai: A sample-efficient hierarchical ai for the minerl competition H Mao, C Wang, X Hao, Y Mao, Y Lu, C Wu, J Hao, D Li, P Tang Distributed Artificial Intelligence: Third International Conference, DAI …, 2022 | 20 | 2022 |
| Towards robust and domain agnostic reinforcement learning competitions: MineRL 2020 WH Guss, S Milani, N Topin, B Houghton, S Mohanty, A Melnik, A Harter, ... NeurIPS 2020 Competition and Demonstration Track, 233-252, 2021 | 10 | 2021 |
| Safe opponent-exploitation subgame refinement M Liu, C Wu, Q Liu, Y Jing, J Yang, P Tang, C Zhang Advances in Neural Information Processing Systems 35, 27610-27622, 2022 | 6 | 2022 |
| Conservative offline policy adaptation in multi-agent games C Wu, P Tang, J Yang, Y Hu, T Lv, C Fan, C Zhang Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| Stylized offline reinforcement learning: Extracting diverse high-quality behaviors from heterogeneous datasets Y Mao, C Wu, X Chen, H Hu, J Jiang, T Zhou, T Lv, C Fan, Z Hu, Y Wu, ... The Twelfth International Conference on Learning Representations, 2024 | 1 | 2024 |
| Towards robust and domain agnostic reinforcement learning competitions WH Guss, S Milani, N Topin, B Houghton, S Mohanty, A Melnik, A Harter, ... arXiv preprint arXiv:2106.03748, 2021 | 1 | 2021 |
Chongjie zhangWashington University in St. Louis在 wustl.edu 的电子邮件经过验证
