Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning Q Li, Z Peng, L Feng, Q Zhang, Z Xue, B Zhou IEEE transactions on pattern analysis and machine intelligence 45 (3), 3461-3475, 2022 | 206 | 2022 |
Swintextspotter: Scene text spotting via better synergy between text detection and text recognition M Huang, Y Liu, Z Peng, C Liu, D Lin, S Zhu, N Yuan, K Ding, L Jin proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 129 | 2022 |
Trafficgen: Learning to generate diverse and realistic traffic scenarios L Feng, Q Li, Z Peng, S Tan, B Zhou 2023 IEEE International Conference on Robotics and Automation (ICRA), 3567-3575, 2023 | 77 | 2023 |
Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization Z Peng, Q Li, KM Hui, C Liu, B Zhou Advances in Neural Information Processing Systems 34, 2021 | 67 | 2021 |
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization Q Li, Z Peng, B Zhou arXiv preprint arXiv:2202.10341, 2022 | 46 | 2022 |
Safe Driving via Expert Guided Policy Optimization Z Peng, Q Li, C Liu, B Zhou 5th Annual Conference on Robot Learning, 2021 | 39 | 2021 |
Scenarionet: Open-source platform for large-scale traffic scenario simulation and modeling Q Li, ZM Peng, L Feng, Z Liu, C Duan, W Mo, B Zhou Advances in neural information processing systems 36, 2024 | 35 | 2024 |
Learning to drive by watching youtube videos: Action-conditioned contrastive policy pretraining Q Zhang, Z Peng, B Zhou European Conference on Computer Vision, 111-128, 2022 | 34 | 2022 |
AXNet: ApproXimate computing using an end-to-end trainable neural network Z Peng, X Chen, C Xu, N Jing, X Liang, C Lu, L Jiang Proceedings of the International Conference on Computer-Aided Design, 1-8, 2018 | 25 | 2018 |
Improving the Generalization of End-to-End Driving through Procedural Generation Q Li, Z Peng, Q Zhang, C Qiu, C Liu, B Zhou arXiv preprint arXiv:2012.13681, 2020 | 21 | 2020 |
Cat: Closed-loop adversarial training for safe end-to-end driving L Zhang, Z Peng, Q Li, B Zhou Conference on Robot Learning, 2357-2372, 2023 | 16 | 2023 |
Novel policy seeking with constrained optimization H Sun, Z Peng, B Dai, J Guo, D Lin, B Zhou arXiv preprint arXiv:2005.10696, 2020 | 16 | 2020 |
Safe exploration by solving early terminated mdp H Sun, Z Xu, M Fang, Z Peng, J Guo, B Dai, B Zhou arXiv preprint arXiv:2107.04200, 2021 | 14 | 2021 |
Non-local policy optimization via diversity-regularized collaborative exploration Z Peng, H Sun, B Zhou arXiv preprint arXiv:2006.07781, 2020 | 14 | 2020 |
Approximate random dropout for DNN training acceleration in GPGPU Z Song, R Wang, D Ru, Z Peng, H Huang, H Zhao, X Liang, L Jiang 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 108-113, 2019 | 13 | 2019 |
Learning from active human involvement through proxy value propagation Z Peng, W Mo, C Duan, Q Li, B Zhou Advances in neural information processing systems 36, 2024 | 11 | 2024 |
Human-AI Shared Control via Policy Dissection Q Li, Z Peng, H Wu, L Feng, B Zhou Advances in Neural Information Processing Systems 35, 8853-8867, 2022 | 10 | 2022 |
Guarded policy optimization with imperfect online demonstrations Z Xue, Z Peng, Q Li, Z Liu, B Zhou arXiv preprint arXiv:2303.01728, 2023 | 8 | 2023 |
Constrained mdps can be solved by eearly-termination with recurrent models H Sun, Z Xu, Z Peng, M Fang, T Wang, B Dai, B Zhou NeurIPS 2022 Foundation Models for Decision Making Workshop, 2022 | 5 | 2022 |
Swintextspotter v2: Towards better synergy for scene text spotting M Huang, D Peng, H Li, Z Peng, C Liu, D Lin, Y Liu, X Bai, L Jin arXiv preprint arXiv:2401.07641, 2024 | 2 | 2024 |