2 citations · 7 across the 8 of their papers we have counts for
7 papers · 1 filter
Accelerating Multi-Task Temporal Difference Learning under Low-Rank Representation
Yitao Bai, Sihan Zeng, Justin Romberg +1
We study policy evaluation problems in multi-task reinforcement learning (RL) under a low-rank representation setting. In this setting, we are given learning tasks where the co…
Nonasymptotic CLT and Error Bounds for Linear Two-Time-Scale Stochastic Approximation
Seo Taek Kong, Sihan Zeng, Thinh T. Doan +1
We consider linear two-time-scale stochastic approximation algorithms driven by martingale noise. Recent applications in machine learning motivate the need to understand finite-tim…
Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
Sihan Zeng, Thinh T. Doan, Justin Romberg
The aim of this paper is to improve the understanding of the optimization landscape for policy optimization problems in reinforcement learning. Specifically, we show that the super…
Sequential Fair Resource Allocation under a Markov Decision Process Framework
Parisa Hassanzadeh, Eleonora Kreacic, Sihan Zeng +2
We study the sequential decision-making problem of allocating a limited resource to agents that reveal their stochastic demands on arrival over a finite horizon. Our goal is to des…
Finite-Time Convergence Rates of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning
Sihan Zeng, Thinh T. Doan, Justin Romberg
We study a decentralized variant of stochastic approximation, a data-driven approach for finding the root of an operator under noisy measurements. A network of agents, each with it…
A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning
Sihan Zeng, Aqeel Anwar, Thinh Doan +2
We develop a mathematical framework for solving multi-task reinforcement learning (MTRL) problems based on a type of policy gradient method. The goal in MTRL is to learn a common p…