2 citations · 2 across the 2 of their papers we have counts for
6 papers
Provably Correct Optimization and Exploration with Non-linear Policies
Fei Feng, Wotao Yin, Alekh Agarwal +1
Policy optimization methods remain a powerful workhorse in empirical Reinforcement Learning (RL), with a focus on neural policies that can easily reason over complex and continuous…
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
Fei Feng, Ruosong Wang, Wotao Yin +2
Motivated by the prevailing paradigm of using unsupervised learning for efficient exploration in reinforcement learning (RL) problems [tang2017exploration,bellemare2016unifying], w…
How Does an Approximate Model Help in Reinforcement Learning?
Fei Feng, Wotao Yin, Lin F. Yang
One of the key approaches to save samples in reinforcement learning (RL) is to use knowledge from an approximate model such as its simulator. However, how much does an approximate…
Acceleration of SVRG and Katyusha X by Inexact Preconditioning
Yanli Liu, Fei Feng, Wotao Yin
Empirical risk minimization is an important class of optimization problems with many popular machine learning applications, and stochastic variance reduction methods are popular ch…
AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity
Yibo Zeng, Fei Feng, Wotao Yin
In this paper, we propose AsyncQVI, an asynchronous-parallel Q-value iteration for discounted Markov decision processes whose transition and reward can only be sampled through a ge…
A2BCD: An Asynchronous Accelerated Block Coordinate Descent Algorithm With Optimal Complexity
Robert Hannah, Fei Feng, Wotao Yin
In this paper, we propose the Asynchronous Accelerated Nonuniform Randomized Block Coordinate Descent algorithm (A2BCD), the first asynchronous Nesterov-accelerated algorithm that…