20 citations · 38 across the 14 of their papers we have counts for
15 papers
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…
Accelerated Multi-Time-Scale Stochastic Approximation: Optimal Complexity and Applications in Reinforcement Learning and Multi-Agent Games
Sihan Zeng, Thinh T. Doan
Multi-time-scale stochastic approximation is an iterative algorithm for finding the fixed point of a set of coupled operators given their noisy samples. It has been observed th…
Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
Sihan Zeng, Thinh T. Doan, Justin Romberg
Multi-task reinforcement learning (RL) aims to find a single policy that effectively solves multiple tasks at the same time. This paper presents a constrained formulation for multi…
Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning
Sihan Zeng, Thinh T. Doan
Two-time-scale optimization is a framework introduced in Zeng et al. (2024) that abstracts a range of policy evaluation and policy optimization problems in reinforcement learning (…
QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints
Sihan Zeng, Youngdae Kim, Yuxuan Ren +1
At the heart of power system operations, alternating current optimal power flow (ACOPF) studies the generation of electric power in the most economical way under network-wide load…
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…
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games
Sihan Zeng, Thinh T. Doan, Justin Romberg
We study the problem of finding the Nash equilibrium in a two-player zero-sum Markov game. Due to its formulation as a minimax optimization program, a natural approach to solve the…
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow
Sihan Zeng, Alyssa Kody, Youngdae Kim +2
With the increasing penetration of distributed energy resources, distributed optimization algorithms have attracted significant attention for power systems applications due to thei…
Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes
Sihan Zeng, Thinh T. Doan, Justin Romberg
We consider a discounted cost constrained Markov decision process (CMDP) policy optimization problem, in which an agent seeks to maximize a discounted cumulative reward subject to…
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning
Sihan Zeng, Thinh T. Doan, Justin Romberg
We study a new two-time-scale stochastic gradient method for solving optimization problems, where the gradients are computed with the aid of an auxiliary variable under samples gen…
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…
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables
Shaojie Xu, Sihan Zeng, Justin Romberg
Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery. In this paper, we propose an algorithm for signal reconstruction f…