30 citations · 109 across the 31 of their papers we have counts for
5 papers · 1 filter
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes
Chonghua Liao, Jiafan He, Quanquan Gu
Reinforcement learning (RL) algorithms can be used to provide personalized services, which rely on users' private and sensitive data. To protect the users' privacy, privacy-preserv…
Learning Stochastic Shortest Path with Linear Function Approximation
Yifei Min, Jiafan He, Tianhao Wang +1
We study the stochastic shortest path (SSP) problem in reinforcement learning with linear function approximation, where the transition kernel is represented as a linear mixture of…
Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL
Weitong Zhang, Jiafan He, Dongruo Zhou +2
The success of deep reinforcement learning (DRL) lies in its ability to learn a representation that is well-suited for the exploration and exploitation task. To understand how the…
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation
Jiafan He, Dongruo Zhou, Quanquan Gu
We study reinforcement learning (RL) with linear function approximation. Existing algorithms for this problem only have high-probability regret and/or Probably Approximately Correc…
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs
Jiafan He, Dongruo Zhou, Quanquan Gu
Learning Markov decision processes (MDPs) in the presence of the adversary is a challenging problem in reinforcement learning (RL). In this paper, we study RL in episodic MDPs with…