| Deep Reinforcement Learning: Fundamentals, Research and Applications H Dong, Z Ding, S Zhang Springer Nature, 2020 | 338* | 2020 |
| Introduction to reinforcement learning Z Ding, Y Huang, H Yuan, H Dong Deep reinforcement learning: fundamentals, research and applications, 47-123, 2020 | 156 | 2020 |
| Challenges of reinforcement learning Z Ding, H Dong Deep Reinforcement Learning: Fundamentals, Research and Applications, 249-272, 2020 | 79 | 2020 |
| Deep reinforcement learning for intelligent transportation systems XY Liu, Z Ding, S Borst, A Walid NIPS Workshop on Machine Learning for Intelligent Transportation Systems 2018, 2018 | 50 | 2018 |
| Sim-to-Real Transfer for Optical Tactile Sensing Z Ding, NF Lepora, E Johns International Conference on Robotics and Automation (ICRA 2020), 2020 | 49 | 2020 |
| Crossing the gap: A deep dive into zero-shot sim-to-real transfer for dynamics E Valassakis, Z Ding, E Johns International Conference on Intelligent Robots and Systems (IROS 2020), 2020 | 43 | 2020 |
| Cdt: Cascading decision trees for explainable reinforcement learning Z Ding, P Hernandez-Leal, GW Ding, C Li, R Huang arXiv preprint arXiv:2011.07553, 2020 | 38 | 2020 |
| Tensor Super-Resolution with Generative Adversarial Nets: A Large Image Generation Approach Z Ding, XY Liu, M Yin, L Kong IJCAI 2019 International Workshop on Human Brain and Artificial Intelligence …, 2019 | 35* | 2019 |
| Arena: A general evaluation platform and building toolkit for multi-agent intelligence Y Song, A Wojcicki, T Lukasiewicz, J Wang, A Aryan, Z Xu, M Xu, Z Ding, ... Proceedings of the AAAI conference on artificial intelligence 34 (05), 7253-7260, 2020 | 33 | 2020 |
| Droid: Minimizing the reality gap using single-shot human demonstration YY Tsai, H Xu, Z Ding, C Zhang, E Johns, B Huang IEEE Robotics and Automation Letters 6 (2), 3168-3175, 2021 | 31 | 2021 |
| Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning Z Ding, C Jin arXiv preprint arXiv:2309.16984, 2023 | 27 | 2023 |
| Fast high-fidelity readout of a single trapped-ion qubit via machine-learning methods ZH Ding, JM Cui, YF Huang, CF Li, T Tu, GC Guo Physical Review Applied 12 (1), 014038, 2019 | 26 | 2019 |
| Sim-to-real transfer for robotic manipulation with tactile sensory Z Ding, YY Tsai, WW Lee, B Huang 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 24 | 2021 |
| Probabilistic mixture-of-experts for efficient deep reinforcement learning J Ren, Y Li, Z Ding, W Pan, H Dong arXiv preprint arXiv:2104.09122, 2021 | 22 | 2021 |
| Diffusion world model: Future modeling beyond step-by-step rollout for offline reinforcement learning Z Ding, A Zhang, Y Tian, Q Zheng arXiv preprint arXiv:2402.03570, 2024 | 21 | 2024 |
| Rlzoo: A comprehensive and adaptive reinforcement learning library Z Ding, T Yu, Y Huang, H Zhang, L Mai, H Dong Proceedings of the 29th ACM International Conference on Multimedia, 3759 …, 2020 | 18* | 2020 |
| Efficient and guaranteed-safe non-convex trajectory optimization with constrained diffusion model A Li, Z Ding, A Bousso Dieng, R Beeson arXiv e-prints, arXiv: 2403.05571, 2024 | 11 | 2024 |
| Learning a universal human prior for dexterous manipulation from human preference Z Ding, Y Chen, AZ Ren, SS Gu, Q Wang, H Dong, C Jin arXiv preprint arXiv:2304.04602, 2023 | 10 | 2023 |
| Multi-agent reinforcement learning for network load balancing in data center Z Yao, Z Ding, T Clausen Proceedings of the 31st ACM International Conference on Information …, 2022 | 9 | 2022 |
| Reinforced workload distribution fairness Z Yao, Z Ding, TH Clausen 5th Workshop on Machine Learning for Systems at 35th Conference on Neural …, 2021 | 8 | 2021 |
Hao DongAssistant Professor at Peking Universitypku.edu.cn üzerinde doğrulanmış e-posta adresine sahip