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The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces


Jun 07, 2021
Chi Jin, Qinghua Liu, Tiancheng Yu


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Provably Efficient Algorithms for Multi-Objective Competitive RL


Feb 05, 2021
Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra


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Provably Efficient Online Agnostic Learning in Markov Games


Oct 28, 2020
Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra


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A Sharp Analysis of Model-based Reinforcement Learning with Self-Play


Oct 04, 2020
Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin


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Near-Optimal Reinforcement Learning with Self-Play


Jul 14, 2020
Yu Bai, Chi Jin, Tiancheng Yu


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A General Framework for Analyzing Stochastic Dynamics in Learning Algorithms


Jun 11, 2020
Chi-Ning Chou, Mien Brabeeba Wang, Tiancheng Yu


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Reward-Free Exploration for Reinforcement Learning


Feb 07, 2020
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu


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Learning Adversarial MDPs with Bandit Feedback and Unknown Transition


Jan 07, 2020
Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu

* Improved the algorithm with a tighter confidence set 

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Efficient Policy Learning for Non-Stationary MDPs under Adversarial Manipulation


Aug 21, 2019
Tiancheng Yu, Suvrit Sra

* There is a problem in the Theorem 1. We will try to fix it and update a new version 

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Near Optimal Stratified Sampling


Jul 26, 2019
Tiancheng Yu, Xiyu Zhai, Suvrit Sra

* We have discovered a mistake in the main result. The quantity on the RHS of (3) is not equal to the variance of estimator (2) when the sampling rule is designed adaptively as we do. There will be further cross-product terms which are now dominant terms. Therefore, although our bound is correct for (3), it no longer implies bound of the variance of (2) 

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Entropy Rate Estimation for Markov Chains with Large State Space


Sep 24, 2018
Yanjun Han, Jiantao Jiao, Chuan-Zheng Lee, Tsachy Weissman, Yihong Wu, Tiancheng Yu

* Published as a conference paper on NIPS 2018 

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