5 papers
Scale-free Adversarial Reinforcement Learning
Mingyu Chen, Xuezhou Zhang
This paper initiates the study of scale-free learning in Markov Decision Processes (MDPs), where the scale of rewards/losses is unknown to the learner. We design a generic algorith…
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
Canzhe Zhao, Ruofeng Yang, Baoxiang Wang +2
In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting. In particular, the unknown transition probability kernel admits…
Federated Multi-Level Optimization over Decentralized Networks
Shuoguang Yang, Xuezhou Zhang, Mengdi Wang
Multi-level optimization has gained increasing attention in recent years, as it provides a powerful framework for solving complex optimization problems that arise in many fields, s…
Improved Algorithms for Adversarial Bandits with Unbounded Losses
Mingyu Chen, Xuezhou Zhang
We consider the Adversarial Multi-Armed Bandits (MAB) problem with unbounded losses, where the algorithms have no prior knowledge on the sizes of the losses. We present UMAB-NN and…
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Kaixuan Huang, Yu Wu, Xuezhou Zhang +4
Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a few seed nodes. Recent studies follow…