61 citations · 750 across the 66 of their papers we have counts for
16 papers · 1 filter
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms
Tengyu Xu, Yingbin Liang
Two timescale stochastic approximation (SA) has been widely used in value-based reinforcement learning algorithms. In the policy evaluation setting, it can model the linear and non…
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee
Tengyu Xu, Yingbin Liang, Guanghui Lan
In safe reinforcement learning (SRL) problems, an agent explores the environment to maximize an expected total reward and meanwhile avoids violation of certain constraints on a num…
Finite-Time Analysis for Double Q-learning
Huaqing Xiong, Lin Zhao, Yingbin Liang +1
Although Q-learning is one of the most successful algorithms for finding the best action-value function (and thus the optimal policy) in reinforcement learning, its implementation…
Bilevel Optimization: Convergence Analysis and Enhanced Design
Kaiyi Ji, Junjie Yang, Yingbin Liang
Bilevel optimization has arisen as a powerful tool for many machine learning problems such as meta-learning, hyperparameter optimization, and reinforcement learning. In this paper,…
Spectral Algorithms for Community Detection in Directed Networks
Zhe Wang, Yingbin Liang, Pengsheng Ji
Community detection in large social networks is affected by degree heterogeneity of nodes. The D-SCORE algorithm for directed networks was introduced to reduce this effect by takin…
Momentum Q-learning with Finite-Sample Convergence Guarantee
Bowen Weng, Huaqing Xiong, Lin Zhao +2
Existing studies indicate that momentum ideas in conventional optimization can be used to improve the performance of Q-learning algorithms. However, the finite-sample analysis for…