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Zhenghao Peng

Hi there! I am a 3rd year PhD student in Computer Science at University of California, Los Angeles, and is supervised by Professor Bolei Zhou. Before moving to UCLA, I already worked with Prof. Zhou and conducted researches for years and earned my MPhil degree in the Multimedia Lab at the Chinese University of Hong Kong. Before that, I got my Bachelor degree at Shanghai Jiao Tong University.

My research interests cover multi-agent behavior modeling and human-in-the-loop agent learning. Hope you enjoy my works!

I am looking for a summer internship in 2025. Please feel free to reach out if you are interested in working with me!

(Pronunciation of my name)

Recent Activities

Sep 25, 2024 Papers on shared autonomy (AI assists human) and diffusion on driving were accepted to NeurIPS 2024.
Jul 3, 2024 Paper on RL finetuning behavior model was accepted to ECCV 2024.
Jun 14, 2024 Have been awarded the Amazon Fellowship. Many thanks to Amazon!
Sep 21, 2023 Human-in-the-loop learning method PVP was accepted to NeurIPS 2023 as Spotlight! ScenarioNet was accepted to NeurIPS 2023 Dataset track!
Jun 19, 2023 I am starting an internship at Waymo! Great to be here at Bay Area!
Jan 28, 2023 TrafficGen on traffic scene generation was accepted to ICRA 2023. TS2C on learning super-teacher agent was accepted to ICLR 2023.
Sep 14, 2022 Policy Dissection was accepted to NeurIPS 2022!
Sep 10, 2022 I moved to UCLA. Go bruins!
Mar 28, 2022 MetaDrive white paper was accepted to TPAMI!
Jan 21, 2022 One paper on Human-AI Copilot (HACO) was accepted to ICLR 2022!

Selected Projects

  1. ECCV
    Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving
    Zhenghao Peng, Wenjie Luo, Yiren Lu, Tianyi Shen, Cole Gulino, Ari Seff, and Justin Fu
    European Conference on Computer Vision (ECCV) , 2024
    Details
  2. NeurIPS Spotlight
    Learning from Active Human Involvement through Proxy Value Propagation
    Zhenghao Peng, Wenjie Mo, Chenda Duan, Quanyi Li, and Bolei Zhou
    Advances in Neural Information Processing Systems (NeurIPS Spotlight) , 2023
    Details
  3. NeurIPS
    ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
    Quanyi Li*, Zhenghao Peng*, Lan Feng*, Zhizheng Liu, Chenda Duan, Wenjie Mo, and Bolei Zhou
    Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track (NeurIPS) , 2023
    Details
  4. CoRL
    CAT: Closed-loop Adversarial Training for Safe End-to-End Driving
    Linrui Zhang, Zhenghao Peng, Quanyi Li, and Bolei Zhou
    7th Annual Conference on Robot Learning (CoRL) , 2023
    Details
  5. ICLR Spotlight
    Guarded Policy Optimization with Imperfect Online Demonstrations
    Zhenghai Xue, Zhenghao Peng, Quanyi Li, Zhihan Liu, and Bolei Zhou
    International Conference on Learning Representation (ICLR Spotlight) , 2023
    Details
  6. ICRA
    TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios
    Lan Feng*, Quanyi Li*, Zhenghao Peng*, Shuhan Tan, and Bolei Zhou
    International Conference on Robotics and Automation (ICRA) , 2023
    Details
  7. NeurIPS
    Human-AI Shared Control via Policy Dissection
    Quanyi Li, Zhenghao Peng, Haibin Wu, Lan Feng, and Bolei Zhou
    Advances in Neural Information Processing Systems (NeurIPS) , 2022
    Details
  8. ICLR
    Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
    Quanyi Li*, Zhenghao Peng*, and Bolei Zhou
    International Conference on Learning Representations (ICLR) , 2022
    Details
  9. NeurIPS
    Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization
    Zhenghao Peng, Quanyi Li, Chunxiao Liu, and Bolei Zhou
    Advances in Neural Information Processing Systems (NeurIPS) , 2021
    Details
  10. CoRL
    Safe Driving via Expert Guided Policy Optimization
    Zhenghao Peng*, Quanyi Li*, Chunxiao Liu, and Bolei Zhou
    5th Annual Conference on Robot Learning (CoRL) , 2021
    Details
  11. TPAMI
    MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
    Quanyi Li*, Zhenghao Peng*, Lan Feng, Qihang Zhang, Zhenghai Xue, and Bolei Zhou
    (TPAMI) , 2021
    Details