2 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…