| Learning robust policy against disturbance in transition dynamics via state-conservative policy optimization Y Kuang, M Lu, J Wang, Q Zhou, B Li, H Li Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7247-7254, 2022 | 17 | 2022 |
| Sample-efficient reinforcement learning via conservative model-based actor-critic Z Wang, J Wang, Q Zhou, B Li, H Li Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8612-8620, 2022 | 15 | 2022 |
| Deep model-based reinforcement learning via estimated uncertainty and conservative policy optimization Q Zhou, HQ Li, J Wang Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6941-6948, 2020 | 15 | 2020 |
| Efficient exploration in resource-restricted reinforcement learning Z Wang, T Pan, Q Zhou, J Wang Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 10279 …, 2023 | 5 | 2023 |
| Promoting stochasticity for expressive policies via a simple and efficient regularization method Q Zhou, Y Kuang, Z Qiu, H Li, J Wang Advances in Neural Information Processing Systems 33, 13504-13514, 2020 | 4 | 2020 |
| Robust representation learning by clustering with bisimulation metrics for visual reinforcement learning with distractions Q Liu, Q Zhou, R Yang, J Wang Proceedings of the AAAI Conference on Artificial Intelligence 37 (7), 8843-8851, 2023 | 3 | 2023 |
| Learning robust representation for reinforcement learning with distractions by reward sequence prediction Q Zhou, J Wang, Q Liu, Y Kuang, W Zhou, H Li Uncertainty in Artificial Intelligence, 2551-2562, 2023 | 1 | 2023 |
| Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution J Wang, R Yang, Z Geng, Z Shi, M Ye, Q Zhou, S Ji, B Li, Y Zhang, F Wu arXiv preprint arXiv:2302.09601, 2023 | 1 | 2023 |
Jie Wang,Professor at University of Science and Technology of ChinaVerified email at ustc.edu.cn
