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20212023
most citedA Reduction-Based Framework for Conservative Bandits and Reinforcement Learning

2 citations · 5 across the 5 of their papers we have counts for

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3 papers · 1 filter

cs.LG2023

Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds

Jiayi Huang, Han Zhong, Liwei Wang +1

While numerous works have focused on devising efficient algorithms for reinforcement learning (RL) with uniformly bounded rewards, it remains an open question whether sample or tim…

quant-ph2023★ 1 cited

Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret

Han Zhong, Jiachen Hu, Yecheng Xue +2

While quantum reinforcement learning (RL) has attracted a surge of attention recently, its theoretical understanding is limited. In particular, it remains elusive how to design pro…

cs.LG2023★ 1 cited

A Reduction-based Framework for Sequential Decision Making with Delayed Feedback

Yunchang Yang, Han Zhong, Tianhao Wu +3

We study stochastic delayed feedback in general multi-agent sequential decision making, which includes bandits, single-agent Markov decision processes (MDPs), and Markov games (MGs…