212 citations · 267 across the 6 of their papers we have counts for
13 papers
Batched Neural Bandits
Quanquan Gu, Amin Karbasi, Khashayar Khosravi +2
In many sequential decision-making problems, the individuals are split into several batches and the decision-maker is only allowed to change her policy at the end of batches. These…
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
Dongruo Zhou, Quanquan Gu, Csaba Szepesvari
We study reinforcement learning (RL) with linear function approximation where the underlying transition probability kernel of the Markov decision process (MDP) is a linear mixture…
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Jiafan He, Dongruo Zhou, Quanquan Gu
Reinforcement learning (RL) with linear function approximation has received increasing attention recently. However, existing work has focused on obtaining -type regret bo…
Provable Multi-Objective Reinforcement Learning with Generative Models
Dongruo Zhou, Jiahao Chen, Quanquan Gu
Multi-objective reinforcement learning (MORL) is an extension of ordinary, single-objective reinforcement learning (RL) that is applicable to many real-world tasks where multiple o…
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
Dongruo Zhou, Jiafan He, Quanquan Gu
Modern tasks in reinforcement learning have large state and action spaces. To deal with them efficiently, one often uses predefined feature mapping to represent states and actions…
Neural Contextual Bandits with UCB-based Exploration
Dongruo Zhou, Lihong Li, Quanquan Gu
We study the stochastic contextual bandit problem, where the reward is generated from an unknown function with additive noise. No assumption is made about the reward function other…