2 citations · 5 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
Haotian Ye, Xiaoyu Chen, Liwei Wang +1
Generalization in Reinforcement Learning (RL) aims to learn an agent during training that generalizes to the target environment. This paper studies RL generalization from a theoret…
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning
Yunchang Yang, Tianhao Wu, Han Zhong +5
In this paper, we present a reduction-based framework for conservative bandits and RL, in which our core technique is to calculate the necessary and sufficient budget obtained from…