| A survey on deep reinforcement learning Q Liu, JW Zhai, ZZ Zhang, S Zhong, Q Zhou, P Zhang, J Xu Chinese Journal of Computers 41 (1), 1-27, 2018 | 209 | 2018 |
| 深度强化学习综述 刘全， 翟建伟， 章宗长， 钟珊， 周倩， 章鹏， 徐进 计算机学报 41 (1), 1-27, 2018 | 130 | 2018 |
| Weighted double Q-learning Z Zhang, Z Pan, MJ Kochenderfer IJCAI-2017, 3455-3461, 2017 | 119 | 2017 |
| A deep Bayesian policy reuse approach against non-stationary agents Y Zheng, Z Meng, J Hao, Z Zhang, T Yang, C Fan NeurIPS-2018, 954-964, 2018 | 96 | 2018 |
| Hierarchical deep multiagent reinforcement learning with temporal abstraction H Tang, J Hao, T Lv, Y Chen, Z Zhang, H Jia, C Ren, Y Zheng, Z Meng, ... arXiv preprint arXiv:1809.09332, 2018 | 82 | 2018 |
| Multi-Agent Incentive Communication via Decentralized Teammate Modeling L Yuan, J Wang, F Zhang, C Wang, Z Zhang, Y Yu, C Zhang AAAI-2022, 9466-9474, 2022 | 69 | 2022 |
| A survey on deep reinforcement learning L Quan, Z Jianwei, Z Zongchang, Z Shan, Z Qian Chinese Journal of Computers 41 (01), 1-27, 2018 | 62 | 2018 |
| Weighted double deep multiagent reinforcement learning in stochastic cooperative environments Y Zheng, Z Meng, J Hao, Z Zhang PRICAI-2018, 421-429, 2018 | 50 | 2018 |
| Adapt to Environment Sudden Changes by Learning a Context Sensitive Policy FM Luo, S Jiang, Y Yu, Z Zhang, YF Zhang AAAI-2022, 7637-7646, 2022 | 43 | 2022 |
| Efficient deep reinforcement learning via adaptive policy transfer T Yang, J Hao, Z Meng, Z Zhang, Y Hu, Y Chen, C Fan, W Wang, W Liu, ... IJCAI-2020, 3094-3100, 2020 | 39 | 2020 |
| Triple-GAIL: A multi-modal imitation learning framework with generative adversarial Nets C Fei, B Wang, Y Zhuang, Z Zhang, J Hao, H Zhang, X Ji, W Liu IJCAI-2020, 2929-2935, 2020 | 38 | 2020 |
| Multi-agent Dynamic Algorithm Configuration K Xue, J Xu, L Yuan, M Li, C Qian, Z Zhang, Y Yu NeurIPS-2022, 20147-20161, 2022 | 37 | 2022 |
| Deep Q-learning with prioritized sampling J Zhai, Q Liu, Z Zhang, S Zhong, H Zhu, P Zhang, C Sun ICONIP-2016, 13-22, 2016 | 34 | 2016 |
| Language Model Self-improvement by Reinforcement Learning Contemplation JC Pang, P Wang, K Li, XH Chen, J Xu, Z Zhang, Y Yu ICLR-2024, 2024 | 32* | 2024 |
| Efficient Multi-agent Communication via Self-supervised Information Aggregation C Guan, F Chen, L Yuan, C Wang, H Yin, Z Zhang, Y Yu NeurIPS-2022, 1020-1033, 2022 | 31 | 2022 |
| Thompson sampling based Monte-Carlo planning in POMDPs A Bai, F Wu, Z Zhang, X Chen ICAPS-2014, 28-36, 2014 | 30 | 2014 |
| Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data F Zhang, C Jia, YC Li, L Yuan, Y Yu, Z Zhang ICLR-2023, 2023 | 27 | 2023 |
| Covering number for efficient heuristic-based POMDP planning Z Zhang, D Hsu, WS Lee ICML-2014, 28-36, 2014 | 26 | 2014 |
| Covering number as a complexity measure for POMDP planning and learning Z Zhang, M Littman, X Chen AAAI-2012, 1853-1859, 2012 | 26 | 2012 |
| Policy Regularization with Dataset Constraint for Offline Reinforcement Learning Y Ran, YC Li, F Zhang, Z Zhang, Y Yu ICML-2023, 28701-28717, 2023 | 25 | 2023 |
Yang YuProfessor, Nanjing Universitynju.edu.cn의 이메일 확인됨