5 papers
A Finite Sample Analysis for Quantile Temporal Difference Learning in Distributional Reinforcement Learning
Zijie Cheng, Xiang Li, Yang Peng +1
We establish a global finite-sample guarantee for synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement learning. The proof separates two…
Online Inference for Quantile Temporal Difference Learning in Distributional Reinforcement Learning
Zijie Cheng, Yang Peng, Zhihua Zhang
In this paper, we study how to perform statistical inference for quantile temporal difference learning (QTD) in distributional reinforcement learning. Assuming access to a generati…
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…