| Qatten: A general framework for cooperative multiagent reinforcement learning Y Yang, J Hao, B Liao, K Shao, G Chen, W Liu, H Tang arXiv preprint arXiv:2002.03939, 2020 | 219 | 2020 |
| Exploration in deep reinforcement learning: a comprehensive survey T Yang, H Tang, C Bai, J Liu, J Hao, Z Meng, P Liu, Z Wang arXiv e-prints, arXiv: 2109.06668, 2021 | 127 | 2021 |
| Exploration in deep reinforcement learning: From single-agent to multiagent domain J Hao, T Yang, H Tang, C Bai, J Liu, Z Meng, P Liu, Z Wang IEEE Transactions on Neural Networks and Learning Systems, 2023 | 107 | 2023 |
| 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 | 81 | 2018 |
| Deep multi-agent reinforcement learning with discrete-continuous hybrid action spaces H Fu, H Tang, J Hao, Z Lei, Y Chen, C Fan arXiv preprint arXiv:1903.04959, 2019 | 79 | 2019 |
| 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 effective context for meta-reinforcement learning: an approach based on contrastive learning H Fu, H Tang, J Hao, C Chen, X Feng, D Li, W Liu Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7457-7465, 2021 | 52 | 2021 |
| Hyar: Addressing discrete-continuous action reinforcement learning via hybrid action representation B Li, H Tang, Y Zheng, J Hao, P Li, Z Wang, Z Meng, L Wang arXiv preprint arXiv:2109.05490, 2021 | 51 | 2021 |
| Kogun: accelerating deep reinforcement learning via integrating human suboptimal knowledge P Zhang, J Hao, W Wang, H Tang, Y Ma, Y Duan, Y Zheng arXiv preprint arXiv:2002.07418, 2020 | 38 | 2020 |
| Pmic: Improving multi-agent reinforcement learning with progressive mutual information collaboration P Li, H Tang, T Yang, X Hao, T Sang, Y Zheng, J Hao, ME Taylor, W Tao, ... arXiv preprint arXiv:2203.08553, 2022 | 32 | 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 | 29 | 2021 |
| What about inputting policy in value function: Policy representation and policy-extended value function approximator H Tang, Z Meng, J Hao, C Chen, D Graves, D Li, C Yu, H Mao, W Liu, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8441-8449, 2022 | 24 | 2022 |
| ERL-Re: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation HAO Jianye, P Li, H Tang, Y Zheng, X Fu, Z Meng The Eleventh International Conference on Learning Representations, 2022 | 22 | 2022 |
| Addressing action oscillations through learning policy inertia C Chen, H Tang, J Hao, W Liu, Z Meng Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7020-7027, 2021 | 16 | 2021 |
| Race: improve multi-agent reinforcement learning with representation asymmetry and collaborative evolution P Li, J Hao, H Tang, Y Zheng, X Fu International Conference on Machine Learning, 19490-19503, 2023 | 15 | 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 | 13 | 2020 |
| ERL-Re: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation J Hao, P Li, H Tang, Y Zheng, X Fu, Z Meng arXiv preprint arXiv:2210.17375, 2022 | 12 | 2022 |
| Large scale deep reinforcement learning in war-games H Wang, H Tang, J Hao, X Hao, Y Fu, Y Ma 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2020 | 12 | 2020 |
| A real-time ensemble classification algorithm for time series data X Zhu, S Zhao, Y Yang, H Tang, Z Wang, J Hao 2017 IEEE International Conference on Agents (ICA), 145-150, 2017 | 11 | 2017 |
| Pandr: Fast adaptation to new environments from offline experiences via decoupling policy and environment representations T Sang, H Tang, Y Ma, J Hao, Y Zheng, Z Meng, B Li, Z Wang arXiv preprint arXiv:2204.02877, 2022 | 7 | 2022 |
Jianye HaoHuawei Noah's Ark Lab/Tianjin UniversityVerified email at tju.edu.cn
