9 citations · 47 across the 24 of their papers we have counts for
11 papers · 1 filter
Iterative Teacher-Aware Learning
Luyao Yuan, Dongruo Zhou, Junhong Shen +5
In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the stude…
Linear Contextual Bandits with Adversarial Corruptions
Heyang Zhao, Dongruo Zhou, Quanquan Gu
We study the linear contextual bandit problem in the presence of adversarial corruption, where the interaction between the player and a possibly infinite decision set is contaminat…
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima
Zixiang Chen, Dongruo Zhou, Quanquan Gu
Escaping from saddle points and finding local minimum is a central problem in nonconvex optimization. Perturbed gradient methods are perhaps the simplest approach for this problem.…
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation
Weitong Zhang, Dongruo Zhou, Quanquan Gu
We study the model-based reward-free reinforcement learning with linear function approximation for episodic Markov decision processes (MDPs). In this setting, the agent works in tw…
Pure Exploration in Kernel and Neural Bandits
Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang +3
We study pure exploration in bandits, where the dimension of the feature representation can be much larger than the number of arms. To overcome the curse of dimensionality, we prop…
Variance-Aware Off-Policy Evaluation with Linear Function Approximation
Yifei Min, Tianhao Wang, Dongruo Zhou +1
We study the off-policy evaluation (OPE) problem in reinforcement learning with linear function approximation, which aims to estimate the value function of a target policy based on…
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…
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games
Zixiang Chen, Dongruo Zhou, Quanquan Gu
We study reinforcement learning for two-player zero-sum Markov games with simultaneous moves in the finite-horizon setting, where the transition kernel of the underlying Markov gam…
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
Tianhao Wang, Dongruo Zhou, Quanquan Gu
We study reinforcement learning (RL) with linear function approximation under the adaptivity constraint. We consider two popular limited adaptivity models: the batch learning model…