50 citations · 194 across the 41 of their papers we have counts for
8 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…
Finite Sample Analysis of Two-Time-Scale Natural Actor-Critic Algorithm
Sajad Khodadadian, Thinh T. Doan, Justin Romberg +1
Actor-critic style two-time-scale algorithms are one of the most popular methods in reinforcement learning, and have seen great empirical success. However, their performance is not…
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…
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms
Thinh T. Doan
Motivated by broad applications in reinforcement learning and federated learning, we study local stochastic approximation over a network of agents, where their goal is to find the…
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…
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation
Thinh T. Doan
Motivated by their broad applications in reinforcement learning, we study the linear two-time-scale stochastic approximation, an iterative method using two different step sizes for…