8 citations · 19 across the 7 of their papers we have counts for
4 papers · 1 filter
RecoMind: A Reinforcement Learning Framework for Optimizing In-Session User Satisfaction in Recommendation Systems
Mehdi Ben Ayed, Fei Feng, Jay Adams +3
Existing web-scale recommendation systems commonly use supervised learning methods that prioritize immediate user feedback. Although reinforcement learning (RL) offers a solution t…
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…