Chenjia Bai 白辰甲
Hi, this is Chenjia Bai. I'm a Ph.D. student in Computer Science at Harbin Institute of Technology (HIT), advised by Prof. Peng Liu. Currently, I am a visiting student at University of Toronto, working with Prof. Animesh Garg . My research mainly focuses on deep Reinforcement Learning (RL), including offline RL, efficient exploration, risk-sensitive learning, and multi-goal RL.
Before that, 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.
Publications
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Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning. [arxiv]
Chenjia Bai, Zhoufan Zhu, Ting Xiao, Lingxiao Wang, Fan Zhou, Peng Liu, and Zhaoran Wang
under review
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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
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Exploration in Deep Reinforcement Learning: A Comprehensive Survey. [arxiv]
Tianpei Yang*, Hongyao Tang*, Chenjia Bai*, Jinyi Liu*, Jianye Hao, Zhaopeng Meng, and Peng Liu (*Equally Contribution)
under review
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Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. [pdf]
Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhihong Deng, Animesh Garg, Peng Liu, and Zhaoran Wang
International Conference on Learning Representations (ICLR), 2022
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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
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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
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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
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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
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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 (TNNLS), 2021 .
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Generating Attentive Goals for Prioritized Hindsight Reinforcement Learning. [pdf]
Peng Liu, Chenjia Bai, Yingnan Zhao, Chenyao Bai, Wei Zhao, and Xianglong Tang (supervisor first-author)
Knowledge-Based Systems (KBS), 2020
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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
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Guided Goal Generation for Hindsight Multi-goal Reinforcement Learning. [pdf]
Chenjia Bai, Peng Liu, Wei Zhao, and Xianglong Tang
Neurocomputing, 2019
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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), ICLR (2021, 2022), ICML (2022), AAAI (2021, 2022)
- Journal Reviewer: IEEE Trans. Cybernetics, IEEE Trans. TNNLS
© 2022 Chenjia Bai