28 citations · 78 across the 16 of their papers we have counts for
5 papers · 2 filters
Stationary Behavior of Constant Stepsize SGD Type Algorithms: An Asymptotic Characterization
Zaiwei Chen, Shancong Mou, Siva Theja Maguluri
Stochastic approximation (SA) and stochastic gradient descent (SGD) algorithms are work-horses for modern machine learning algorithms. Their constant stepsize variants are preferre…
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators
Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai +1
In temporal difference (TD) learning, off-policy sampling is known to be more practical than on-policy sampling, and by decoupling learning from data collection, it enables data re…
Finite-Sample Analysis of Off-Policy Natural Actor-Critic with Linear Function Approximation
Zaiwei Chen, Sajad Khodadadian, Siva Theja Maguluri
In this paper, we develop a novel variant of off-policy natural actor-critic algorithm with linear function approximation and we establish a sample complexity of $\mathcal{O}(ε^{-3…
Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm
Sajad Khodadadian, Zaiwei Chen, Siva Theja Maguluri
In this paper, we provide finite-sample convergence guarantees for an off-policy variant of the natural actor-critic (NAC) algorithm based on Importance Sampling. In particular, we…
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants
Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai +1
This paper develops an unified framework to study finite-sample convergence guarantees of a large class of value-based asynchronous reinforcement learning (RL) algorithms. We do th…