3 citations · 3 across the 7 of their papers we have counts for
7 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…
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…
Federated Reinforcement Learning with Constraint Heterogeneity
Hao Jin, Liangyu Zhang, Zhihua Zhang
We study a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. In our setting, we aim to solve a reinforcement learning problem with multiple constraint…
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…
Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptotics
Wenhao Yang, Liangyu Zhang, Zhihua Zhang
In this paper, we study the non-asymptotic and asymptotic performances of the optimal robust policy and value function of robust Markov Decision Processes(MDPs), where the optimal…