| 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 |
| Free market of multi-leader multi-follower mobile crowdsensing: An incentive mechanism design by deep reinforcement learning Y Zhan, CH Liu, Y Zhao, J Zhang, J Tang IEEE Transactions on Mobile Computing 19 (10), 2316-2329, 2019 | 74 | 2019 |
| Social-aware incentive mechanism for vehicular crowdsensing by deep reinforcement learning Y Zhao, CH Liu IEEE Transactions on Intelligent Transportation Systems 22 (4), 2314-2325, 2020 | 43 | 2020 |
| 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 |
| Cadre: A cascade deep reinforcement learning framework for vision-based autonomous urban driving Y Zhao, K Wu, Z Xu, Z Che, Q Lu, J Tang, CH Liu Proceedings of the AAAI Conference on Artificial Intelligence 36 (3), 3481-3489, 2022 | 20 | 2022 |
| A Minimalist Ensemble Method for Generalizable Offline Deep Reinforcement Learning K Wu, Y Zhao, Z Xu, Z Zhao, P Ren, Z Che, CH Liu, F Feng, J Tang ICLR 2022 Workshop on Generalizable Policy Learning in Physical World, 0 | 2* | |
