10 citations · 10 across the 12 of their papers we have counts for
13 papers
A Finite-Sample Analysis of Quantile Temporal-Difference Learning
Zijie Cheng, Xiang Li, Yang Peng +1
Quantile temporal-difference learning (QTD) is an effective method for learning return distributions through quantile approximation, yet its finite-time behavior remains poorly und…
Online Inference for Quantile Distributional Reinforcement Learning
Zijie Cheng, Yang Peng, Zhihua Zhang
In this paper, we study online statistical inference for quantile-based distributional policy evaluation, which aims to characterize the distribution of discounted cumulative rewar…
Statistical Efficiency and Inference of Quantile Distributional Reinforcement Learning
Zijie Cheng, Yang Peng, Zhihua Zhang
In this paper, we study quantile-based distributional reinforcement learning from the perspective of statistical efficiency. We focus on distributional policy evaluation, whose goa…
Accelerated Distributional Temporal Difference Learning with Linear Function Approximation
Kaicheng Jin, Yang Peng, Jiansheng Yang +1
In this paper, we study the finite-sample statistical rates of distributional temporal difference (TD) learning with linear function approximation. The purpose of distributional TD…
Matrix Moment and Concentration Inequalities for Martingales and Ergodic Markov Chains with Applications in Statistical Learning
Yang Peng, Yuchen Xin, Zhihua Zhang
In this paper, we study moment and concentration inequalities for the spectral norm of sums of dependent random matrices. We establish novel Rosenthal-Burkholder inequalities for t…
A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation
Yang Peng, Kaicheng Jin, Liangyu Zhang +1
In this paper, we study the finite-sample statistical rates of distributional temporal difference (TD) learning with linear function approximation. The aim of distributional TD lea…
Federated Control in Markov Decision Processes
Hao Jin, Yang Peng, Liangyu Zhang +1
We study problems of federated control in Markov Decision Processes. To solve an MDP with large state space, multiple learning agents are introduced to collaboratively learn its op…
Statistical Efficiency of Distributional Temporal Difference Learning and Freedman's Inequality in Hilbert Spaces
Yang Peng, Liangyu Zhang, Zhihua Zhang
Distributional reinforcement learning (DRL) has achieved empirical success in various domains. One core task in DRL is distributional policy evaluation, which involves estimating t…
Gradient Tracking for High Dimensional Federated Optimization
Jiadong Liang, Yang Peng, Zhihua Zhang
In this paper, we study the (decentralized) distributed optimization problem with high-dimensional sparse structure. Building upon the FedDA algorithm, we propose a (Decentralized)…
Estimation and Inference in Distributional Reinforcement Learning
Liangyu Zhang, Yang Peng, Jiadong Liang +2
In this paper, we study distributional reinforcement learning from the perspective of statistical efficiency. We investigate distributional policy evaluation, aiming to estimate th…
Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning
Liangyu Zhang, Yang Peng, Wenhao Yang +1
We propose a novel generalization of constrained Markov decision processes (CMDPs) that we call the \emph{semi-infinitely constrained Markov decision process} (SICMDP). Particularl…
Federated Reinforcement Learning with Environment Heterogeneity
Hao Jin, Yang Peng, Wenhao Yang +2
We study a Federated Reinforcement Learning (FedRL) problem in which agents collaboratively learn a single policy without sharing the trajectories they collected during agent-e…
Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation
Yang Peng, Ping Liu, Yawei Luo +3
Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning…