| Recruitment-imitation mechanism for evolutionary reinforcement learning S Lü, S Han, W Zhou, J Zhang Information Sciences 553, 172-188, 2021 | 22 | 2021 |
| Proximal policy optimization via enhanced exploration efficiency J Zhang, Z Zhang, S Han, S Lü Information Sciences 609, 750-765, 2022 | 19 | 2022 |
| Regularly updated deterministic policy gradient algorithm S Han, W Zhou, S Lü, J Yu Knowledge-Based Systems 214, 106736, 2021 | 16 | 2021 |
| NROWAN-DQN: A stable noisy network with noise reduction and online weight adjustment for exploration S Han, W Zhou, J Liu, S Lü arXiv preprint arXiv:2006.10980, 2020 | 14 | 2020 |
| Model-based Sparse Communication in Multi-agent Reinforcement Learning S Han, M Dastani, S Wang Proceedings of the 2023 International Conference on Autonomous Agents and …, 2023 | 3 | 2023 |
| Deep recurrent deterministic policy gradient for physical control L Zhang, S Han, Z Zhang, L Li, S Lü Artificial Neural Networks and Machine Learning–ICANN 2020: 29th …, 2020 | 3 | 2020 |
| Explorer-Actor-Critic: Better Actors for Deep Reinforcement Learning J Zhang, S Han, X Xiong, S Zhu, S Lü Information Sciences, 120255, 2024 | 2 | 2024 |
| Entropy regularization methods for parameter space exploration S Han, W Zhou, S Lü, S Zhu, X Gong Information Sciences 622, 476-489, 2023 | 2 | 2023 |
| Sampling diversity driven exploration with state difference guidance J Lu, S Han, S Lü, M Kang, J Zhang Expert Systems with Applications 203, 117418, 2022 | 1 | 2022 |
| Mixed experience sampling for off-policy reinforcement learning J Yu, J Li, S Lü, S Han Expert Systems with Applications, 124017, 2024 | | 2024 |
| Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks S Han, M Dastani, S Wang arXiv preprint arXiv:2401.14226, 2024 | | 2024 |
| Guided deterministic policy optimization with gradient-free policy parameters information C Shen, S Zhu, S Han, X Gong, S Lü Expert Systems with Applications 231, 120693, 2023 | | 2023 |
