Working Papers
Working Papers
Non-asymptotic guarantees for average-reward Q-learning with adaptive stepsizes
Zaiwei Chen
Working paper - Submitted.
Zaiwei Chen, Sheng Zhang, Zhe Zhang, Shaan Ul Haque, and Siva Theja Maguluri
Working paper - Submitted.
Maximizing the value of predictions in control: Accuracy is not enough
Yiheng Lin, Christopher Yeh, Zaiwei Chen, and Adam Wierman
Working paper - Submitted.
Last-iterate convergence of payoff-based independent learning in zero-sum stochastic games
Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman Ozdaglar, and Adam Wierman
Working paper - Submitted.
Concentration of contractive stochastic approximation: Additive and multiplicative noise
Zaiwei Chen, Siva Theja Maguluri, and Martin Zubeldia
The Annals of Applied Probability, 2025
An approximate policy iteration viewpoint of actor–critic algorithms
Zaiwei Chen and Siva Theja Maguluri
Automatica, 2024
A Lyapunov theory for finite-sample guarantees of Markovian stochastic approximation
Zaiwei Chen, Siva Theja Maguluri, Karthikeyan Shanmugam, and Sanjay Shakkottai
Operations Research, 2023
Target network and truncation overcome the deadly triad in Q-learning
Zaiwei Chen, John-Paul Clarke, and Siva Theja Maguluri
SIAM Journal on Mathematics of Data Science, 2023
Global convergence of localized policy iteration in networked multi-agent reinforcement learning
Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, and Adam Wierman
Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2023
Stationary behavior of constant stepsize SGD-type algorithms: An asymptotic characterization
Zaiwei Chen*, Shancong Mou*, and Siva Theja Maguluri
Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2022
Zaiwei Chen, Sheng Zhang, Thinh T. Doan, John-Paul Clarke, and Siva Theja Maguluri
Automatica, 2022
Finite-sample analysis of off-policy natural actor–critic with linear function approximation
Zaiwei Chen*, Sajad Khodadadian*, and Siva Theja Maguluri
IEEE Control Systems Letters, 2022
Nested vehicle routing problem: Optimizing drone-truck surveillance operations
Fanruiqi Zeng, Zaiwei Chen, John-Paul Clarke, and David Goldsman
Transportation Research Part C, 2022
Overcoming the curse of dimensionality in reinforcement learning through approximate factorization
Chenbei Lu, Laixi Shi, Zaiwei Chen, Chenye Wu, and Adam Wierman
ICML, 2025
Approximate global convergence of independent learning in multi-agent systems
Ruiyang Jin, Zaiwei Chen, Yiheng Lin, Jie Song, and Adam Wierman
AISTATS, 2025
Last-iterate convergence for generalized Frank-Wolfe in monotone variational inequalities
Zaiwei Chen and Eric Mazumdar
NeurIPS, 2024
Two-timescale Q-learning with function approximation in zero-sum stochastic games
Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman Ozdaglar, and Adam Wierman
The ACM Conference on Economics and Computation, 2024
Convergence rates for localized actor-critic in networked Markov potential games
Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, and Adam Wierman
The Conference on Uncertainty in Artificial Intelligence, 2023
Finite-sample analysis of off-policy TD-learning via generalized Bellman operators
Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, and Karthikeyan Shanmugam
NeurIPS, 2021
Finite-sample analysis of off-policy natural actor-critic algorithm
Sajad Khodadadian*, Zaiwei Chen*, and Siva Theja Maguluri
ICML, 2021
Finite-sample analysis of contractive stochastic approximation using smooth convex envelopes
Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, and Karthikeyan Shanmugam
NeurIPS, 2020