Reinforcement Learning
Reinforcement Learning
(α-β order) denotes alphabetical ordering, * denotes equal contribution.
Learning a Universal Human Prior for Dexterous Manipulation from Human Preference. [arXiv]
Zihan Ding, Yuanpei Chen, Allen Z Ren, Shixiang Shane Gu, Hao Dong, Chi Jin
ArXiv Preprint
Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning [arXiv]
Zihan Ding, Chi Jin
International Conference on Learning Representations (ICLR) 2024
Is RLHF More Difficult than Standard RL? [arXiv]
Yuanhao Wang, Qinghua Liu, Chi Jin
Neural Information Processing Systems (NIPS) 2023
Context-lumpable Stochastic Bandits. [arXiv]
Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesv´ari
Neural Information Processing Systems (NIPS) 2023
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. [arXiv]
Qinghua Liu, Gell´ert Weisz, Andras Gyorgy, Chi Jin, Csaba Szepesv´ari
Neural Information Processing Systems (NIPS) 2023
Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making [arXiv]
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvari, Chi Jin
Symposium on Theory of Computing (STOC) 2023
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations [arXiv]
Jiachen Hu, Han Zhong, Chi Jin, Liwei Wang
International Conference on Learning Representations (ICLR) 2023.
When Is Partially Observable Reinforcement Learning Not Scary? [arXiv]
Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin
Conference of Learning Theory (COLT) 2022.
Provable Reinforcement Learning with a Short-Term Memory. [arXiv]
(α-β order) Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi
International Conference on Machine Learning (ICML) 2022.
A Simple Reward-free Approach to Constrained Reinforcement Learning [arXiv]
International Conference on Machine Learning (ICML) 2022.
Understanding Domain Randomization for Sim-to-real Transfer [arXiv]
International Conference on Learning Representations (ICLR) 2022
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms [arXiv]
(α-β order) Chi Jin, Qinghua Liu, Sobhan Miryoosefi
Neural Information Processing Systems (NIPS) 2021.
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning [arXiv]
(α-β order) Yaqi Duan, Chi Jin, Zhiyuan Li
International Conference on Machine Learning (ICML) 2021.
Near-optimal Representation Learning for Linear Bandits and Linear RL [arXiv]
Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang
International Conference on Machine Learning (ICML) 2021.
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs [arXiv]
(α-β order) Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu
Neural Information Processing Systems (NIPS) 2020.
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces [arXiv]
Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan
Neural Information Processing Systems (NIPS) 2020.
Reward-Free Exploration for Reinforcement Learning [arXiv]
(α-β order) Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
International Conference on Machine Learning (ICML) 2020.
Learning Adversarial MDPs with Bandit Feedback and Unknown Transition [arXiv]
(α-β order) Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu
International Conference on Machine Learning (ICML) 2020.
Provably Efficient Exploration in Policy Optimization [arXiv]
Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang
International Conference on Machine Learning (ICML) 2020.
Provably Efficient Reinforcement Learning with Linear Function Approximation [arXiv]
Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
Conference of Learning Theory (COLT) 2020
Is Q-learning Provably Efficient? [arXiv]
Chi Jin*, Zeyuan Allen-Zhu*, Sebastien Bubeck, Michael I. Jordan
Neural Information Processing Systems (NIPS) 2018. Best Paper in ICML 2018 workshop "Exploration in RL"