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A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis

Nov 11, 2020
Tengyu Xu, Yingbin Liang, Guanghui Lan

* Submitted for conference publication 

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Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms

Nov 10, 2020
Tengyu Xu, Yingbin Liang

* Submitted for conference publication 

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When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence

Jun 25, 2020
Ziwei Guan, Tengyu Xu, Yingbin Liang

* Submitted for publication 

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Enhanced First and Zeroth Order Variance Reduced Algorithms for Min-Max Optimization

Jun 17, 2020
Tengyu Xu, Zhe Wang, Yingbin Liang, H. Vincent Poor

* 43 pages, 6 figures 

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Non-asymptotic Convergence Analysis of Two Time-scale (Natural) Actor-Critic Algorithms

May 08, 2020
Tengyu Xu, Zhe Wang, Yingbin Liang

* The results of this paper were initially submitted for publication in February 2020 

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Improving Sample Complexity Bounds for Actor-Critic Algorithms

Apr 28, 2020
Tengyu Xu, Zhe Wang, Yingbin Liang

* 30 pages, 0 figure 

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Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling

Feb 15, 2020
Huaqing Xiong, Tengyu Xu, Yingbin Liang, Wei Zhang


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Reanalysis of Variance Reduced Temporal Difference Learning

Jan 10, 2020
Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang

* To appear in ICLR 2020 

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Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples

Sep 26, 2019
Tengyu Xu, Shaofeng Zou, Yingbin Liang

* To appear in NeurIPS 2019 

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Finite-Sample Analysis for SARSA and Q-Learning with Linear Function Approximation

Feb 06, 2019
Shaofeng Zou, Tengyu Xu, Yingbin Liang


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When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models?

Oct 15, 2018
Tengyu Xu, Yi Zhou, Kaiyi Ji, Yingbin Liang


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