| Distributed and energy-efficient mobile crowdsensing with charging stations by deep reinforcement learning CH Liu, Z Dai, Y Zhao, J Crowcroft, D Wu, KK Leung IEEE Transactions on Mobile Computing 20 (1), 130-146, 2019 | 89 | 2019 |
| Multi-task-oriented vehicular crowdsensing: A deep learning approach CH Liu, Z Dai, H Yang, J Tang IEEE INFOCOM 2020-IEEE Conference on Computer Communications, 1123-1132, 2020 | 34 | 2020 |
| Aoi-minimal uav crowdsensing by model-based graph convolutional reinforcement learning Z Dai, CH Liu, Y Ye, R Han, Y Yuan, G Wang, J Tang IEEE INFOCOM 2022-IEEE Conference on Computer Communications, 1029-1038, 2022 | 30 | 2022 |
| Delay-sensitive energy-efficient UAV crowdsensing by deep reinforcement learning Z Dai, CH Liu, R Han, G Wang, KK Leung, J Tang IEEE Transactions on Mobile Computing 22 (4), 2038-2052, 2021 | 28 | 2021 |
| Curiosity-driven energy-efficient worker scheduling in vehicular crowdsourcing: A deep reinforcement learning approach CH Liu, Y Zhao, Z Dai, Y Yuan, G Wang, D Wu, KK Leung 2020 IEEE 36th International conference on data engineering (ICDE), 25-36, 2020 | 26 | 2020 |
| Mobile crowdsensing for data freshness: A deep reinforcement learning approach Z Dai, H Wang, CH Liu, R Han, J Tang, G Wang IEEE INFOCOM 2021-IEEE Conference on Computer Communications, 1-10, 2021 | 22 | 2021 |
| Time-aware location prediction by convolutional area-of-interest modeling and memory-augmented attentive lstm CH Liu, Y Wang, C Piao, Z Dai, Y Yuan, G Wang, D Wu IEEE Transactions on Knowledge and Data Engineering, 2020 | 21 | 2020 |
| Energy-efficient 3D vehicular crowdsourcing for disaster response by distributed deep reinforcement learning H Wang, CH Liu, Z Dai, J Tang, G Wang Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 19 | 2021 |
| Socially-attentive policy optimization in multi-agent self-driving system Z Dai, T Zhou, K Shao, DH Mguni, B Wang, HAO Jianye Conference on Robot Learning, 946-955, 2023 | 5 | 2023 |
| Timing is Everything: Learning to act selectively with costly actions and budgetary constraints D Mguni, A Sootla, J Ziomek, O Slumbers, Z Dai, K Shao, J Wang arXiv preprint arXiv:2205.15953, 2022 | 5 | 2022 |
| Exploring both individuality and cooperation for air-ground spatial crowdsourcing by multi-agent deep reinforcement learning Y Ye, CH Liu, Z Dai, J Zhao, Y Yuan, G Wang, J Tang 2023 IEEE 39th International Conference on Data Engineering (ICDE), 205-217, 2023 | 4 | 2023 |
| Learning to shape rewards using a game of two partners D Mguni, T Jafferjee, J Wang, N Perez-Nieves, W Song, F Tong, M Taylor, ... Proceedings of the AAAI Conference on Artificial Intelligence 37 (10), 11604 …, 2023 | 2 | 2023 |
| Cooperative Multi-Agent Transfer Learning with Coalition Pattern Decomposition T Zhou, F Zhang, K Shao, Z Dai, K Li, W Huang, W Wang, B Wang, D Li, ... IEEE Transactions on Games, 2023 | 1 | 2023 |
| QoI-Aware Mobile Crowdsensing for Metaverse by Multi-Agent Deep Reinforcement Learning Y Ye, H Wang, CH Liu, Z Dai, G Li, G Wang, J Tang IEEE Journal on Selected Areas in Communications, 2023 | | 2023 |
| HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning H Wang, B Tang, CH Liu, S Mao, J Zhou, Z Dai, Y Sun, Q Xie, X Wang, ... IEEE Transactions on Computers, 2023 | | 2023 |
| Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction T Jafferjee, J Ziomek, T Yang, Z Dai, J Wang, M Taylor, K Shao, J Wang, ... arXiv preprint arXiv:2209.01054, 2022 | | 2022 |
| Semi-Centralised Multi-Agent Reinforcement Learning with Policy-Embedded Training T Jafferjee, J Ziomek, T Yang, Z Dai, J Wang, M Taylor, K Shao, J Wang, ... arXiv preprint arXiv:2209.01054, 2022 | | 2022 |
| 深度强化学习：学术前沿与实战应用 | | 2020 |
Chi Harold LiuProfessor, Vice Dean, Fellow of IET and BCS, Beijing Institute of TechnologyVerified email at bit.edu.cn
