10 citations · 20 across the 6 of their papers we have counts for
6 papers · 1 filter
Fast Rates for Offline Q-Learning with KL Regularization
Yize Hao, Guangxin Jiang, Shuang Qiu
This paper investigates offline Q-learning for finite-horizon Markov decision processes with Kullback-Leibler (KL) regularization. Recent studies have shown that KL regularization…
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
Shuang Qiu, Ziyu Dai, Han Zhong +3
This paper investigates posterior sampling algorithms for competitive reinforcement learning (RL) in the context of general function approximations. Focusing on zero-sum Markov gam…
On the Value of Myopic Behavior in Policy Reuse
Kang Xu, Chenjia Bai, Shuang Qiu +5
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence. In reinforcement learning, rationally reusing the policies acquired from other tasks or…
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach
Shuang Qiu, Boxiang Lyu, Qinglin Meng +3
Dynamic mechanism design studies how mechanism designers should allocate resources among agents in a time-varying environment. We consider the problem where the agents interact wit…
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
Shupeng Gui, Xiangliang Zhang, Pan Zhong +5
Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recomm…
Proximal Online Gradient is Optimum for Dynamic Regret
Yawei Zhao, Shuang Qiu, Ji Liu
In online learning, the dynamic regret metric chooses the reference (optimal) solution that may change over time, while the typical (static) regret metric assumes the reference sol…