3 citations · 6 across the 10 of their papers we have counts for
10 papers
Steady-State Approximation Error of Heterogeneous Mean-Field Models
Lei Ying
This paper studies heterogeneous mean-field models in which agent parameters are sampled from a population distribution. We establish an bound on the steady-state mean-squ…
On the Approximation and Convergence of Distributional Policy Gradient Algorithms for Risk-Sensitive Reinforcement Learning
Minheng Xiao, Xian Yu, Lei Ying
Risk-sensitive reinforcement learning (RL) is crucial for maintaining reliable performance in high-stakes applications. While traditional RL methods aim to learn a point estimate o…
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
Honghao Wei, Xin Liu, Weina Wang +1
This paper considers a class of reinforcement learning problems, which involve systems with two types of states: stochastic and pseudo-stochastic. In such systems, stochastic state…
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
Honghao Wei, Arnob Ghosh, Ness Shroff +2
We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected…
Online Nonstochastic Control with Adversarial and Static Constraints
Xin Liu, Zixian Yang, Lei Ying
This paper studies online nonstochastic control problems with adversarial and static constraints. We propose online nonstochastic control algorithms that achieve both sublinear reg…
Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach
Kaiyi Ji, Lei Ying
Fair resource allocation is one of the most important topics in communication networks. Existing solutions almost exclusively assume each user utility function is known and concave…
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems
Xin Liu, Honghao Wei, Lei Ying
This paper studies a class of multi-agent reinforcement learning (MARL) problems where the reward that an agent receives depends on the states of other agents, but the next state o…
Learning While Scheduling in Multi-Server Systems with Unknown Statistics: MaxWeight with Discounted UCB
Zixian Yang, R. Srikant, Lei Ying
Multi-server queueing systems are widely used models for job scheduling in machine learning, wireless networks, crowdsourcing, and healthcare systems. This paper considers a multi-…
On Low-Complexity Quickest Intervention of Mutated Diffusion Processes Through Local Approximation
Qining Zhang, Honghao Wei, Weina Wang +1
We consider the problem of controlling a mutated diffusion process with an unknown mutation time. The problem is formulated as the quickest intervention problem with the mutation m…
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
Zixian Yang, Xin Liu, Lei Ying
Multi-armed bandit (MAB) is a classic model for understanding the exploration-exploitation trade-off. The traditional MAB model for recommendation systems assumes the user stays in…