20 citations · 38 across the 14 of their papers we have counts for
4 papers · 1 filter
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…