49 citations · 79 across the 9 of their papers we have counts for
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cs.LG2022★ 1 cited
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
Jinglin Chen, Aditya Modi, Akshay Krishnamurthy +2
We study reward-free reinforcement learning (RL) under general non-linear function approximation, and establish sample efficiency and hardness results under various standard struct…
cs.LG2022★ 1 cited
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: The Power of Gaps
Jinglin Chen, Nan Jiang
We consider a challenging theoretical problem in offline reinforcement learning (RL): obtaining sample-efficiency guarantees with a dataset lacking sufficient coverage, under only…
cs.LG2022★ 4 cited
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality
Jiawei Huang, Jinglin Chen, Li Zhao +3
Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL). Despite the community's increasing interest, there lacks a formal t…