50 citations · 194 across the 41 of their papers we have counts for
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Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation
Thinh T. Doan, Justin Romberg
Two-time-scale stochastic approximation is a popular iterative method for finding the solution of a system of two equations. Such methods have found broad applications in many area…
A Reinforcement Learning Framework for Sequencing Multi-Robot Behaviors
Pietro Pierpaoli, Thinh T. Doan, Justin Romberg +1
Given a list of behaviors and associated parameterized controllers for solving different individual tasks, we study the problem of selecting an optimal sequence of coordinated beha…
Finite-Time Performance of Distributed Temporal Difference Learning with Linear Function Approximation
Thinh T. Doan, Siva Theja Maguluri, Justin Romberg
We study the policy evaluation problem in multi-agent reinforcement learning, modeled by a Markov decision process. In this problem, the agents operate in a common environment unde…
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning
Zaiwei Chen, Sheng Zhang, Thinh T. Doan +2
Motivated by applications in reinforcement learning (RL), we study a nonlinear stochastic approximation (SA) algorithm under Markovian noise, and establish its finite-sample conver…
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation for Multi-Agent Reinforcement Learning
Thinh T. Doan, Siva Theja Maguluri, Justin Romberg
We study the policy evaluation problem in multi-agent reinforcement learning. In this problem, a group of agents works cooperatively to evaluate the value function for the global d…