Chenjia Bai   白辰甲

I am a Researcher at Shanghai AI Laboratory. Prior to this, I obtained my Ph.D. degree in Computer Science from Harbin Institute of Technology (HIT), advised by Prof. Peng Liu. My research mainly focuses on deep Reinforcement Learning (RL), including offline RL, robust RL, efficient exploration, representation learning, risk-sensitive learning, and multi-agent RL.

I am fortunate to have been collaborated with many fantastic researchers. I was a visiting student at University of Toronto and Vector Institute, working with Prof. Animesh Garg . I was a visiting student at Northwestern University (remotely), working with Prof. Zhaoran Wang . I also used to be an intern at Huawei Noah's Ark Lab (advised by Prof. Jianye Hao), Tencent Robotics X (advised by Dr. Lei Han), and Alibaba. I received my Bachelor's degree and Master's degree in Computer Science from HIT.

Internship chances:
Our group is looking for highly-motivated Interns on board Reinforcement Learning research. We are also interested in RL applications including Robot Arm and Quadruped. Please drop me an email if you are interested in.

Publications

  • Lightweight Uncertainty for Offline Reinforcement Learning via Bayesian Posterior.
    Xudong Yu, Chenjia Bai*, Hongyi Guo, Changhong Wang*, Zhen Wang, and Xuelong Li
    under review
  • Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement Learning.
    Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhen Wang, Bin Zhao, and Xuelong Li
    under review
  • SCORE: Spurious Correlation Reduction for Offline Reinforcement Learning. [arxiv]
    Zhihong Deng, Zuyue Fu, Lingxiao Wang, Zhuoran Yang, Chenjia Bai, Zhaoran Wang, and Jing Jiang
    under review
  • RORL: Robust Offline Reinforcement Learning via Conservative Smoothing. [arxiv]
    Rui Yang*, Chenjia Bai*, Xiaoteng Ma, Zhaoran Wang, Chongjie Zhang, Lei Han
    Neural Information Processing Systems (NeurIPS), 2022    Spotlight
  • Self-Supervised Imitation for Offline Reinforcement Learning with Hindsight Relabeling. [pdf]
    Xudong Yu, Chenjia Bai*, Changhong Wang*, Zhen Wang
    IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2022 (under review)
  • Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. [arxiv, code]
    Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, and Zhaoran Wang
    International Conference on Machine Learning (ICML), 2022
  • Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning. [pdf]
    Chenjia Bai, Ting Xiao, Zhoufan Zhu, Lingxiao Wang, Fan Zhou, Peng Liu, and Zhaoran Wang
    IEEE Transactions on Neural Networks and Learning Systems, 2022
  • Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain. [pdf, arxiv]
    Jianye Hao, Tianpei Yang, Hongyao Tang, Chenjia Bai, Jinyi Liu, Zhaopeng Meng, Peng Liu, and Zhen Wang
    IEEE Transactions on Neural Networks and Learning Systems, 2022
  • Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. [pdf, arxiv, code]
    Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhihong Deng, Animesh Garg, Peng Liu, and Zhaoran Wang
    International Conference on Learning Representations (ICLR), 2022    Spotlight
  • OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning. [pdf]
    Jinyi Liu, Wang Zhi, Yan Zheng, Jianye Hao, Junjie Ye, Chenjia Bai, Pengyi Li
    NeurIPS Deep RL Workshop, 2021
  • Dynamic Bottleneck for Robust Self-Supervised Exploration. [arxiv, code]
    Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, and Zhaoran Wang
    Neural Information Processing Systems (NeurIPS), 2021
  • Principled Exploration via Optimistic Bootstrapping and Backward Induction . [pdf, arxiv, code]
    Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, and Zhaoran Wang
    International Conference on Machine Learning (ICML), 2021    Spotlight
  • Addressing Hindsight Bias in Multi-Goal Reinforcement Learning. [pdf, code]
    Chenjia Bai, Lingxiao Wang, Yixin Wang, Zhaoran Wang, Rui Zhao, Chenyao Bai and Peng Liu
    IEEE Transactions on Cybernetics, 2021
  • Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning. [arxiv, website]
    Chenjia Bai, Peng Liu, Kaiyu Liu, Lingxiao Wang, Yingnan Zhao, Lei Han, and Zhaoran Wang
    IEEE Transactions on Neural Networks and Learning Systems, 2021 .
  • Generating Attentive Goals for Prioritized Hindsight Reinforcement Learning. [pdf]
    Peng Liu, Chenjia Bai, Yingnan Zhao, Chenyao Bai, Wei Zhao, and Xianglong Tang
    Knowledge-Based Systems (KBS), 2020
  • Obtaining Accurate Estimated Action Values in Categorical Distributional Reinforcement Learning. [pdf]
    Yingnan Zhao, Peng Liu, Chenjia Bai, Wei Zhao, and Xianglong Tang
    Knowledge-Based Systems (KBS), 2020
  • Active Sampling for Deep Q-learning Based on TD-error Adaptive Correction. [pdf]
    Chenjia Bai, Peng Liu, Wei Zhao, and Xianglong Tang
    Journal of Computer Research and Development (in Chinese), 2019.

Service

  • Conference Reviewer/Program Committee: NeurIPS (2021, 2022), ICLR (2021, 2022), ICML (2022), AAAI (2021, 2022)
  • Journal Reviewer: IEEE Trans. Cybernetics, IEEE Trans. TNNLS

© 2022 Chenjia Bai