2.6k citations · 6.5k across the 69 of their papers we have counts for
8 papers · 1 filter
Deep Reinforcement Learning with a Natural Language Action Space
Ji He, Jianshu Chen, Xiaodong He +4
This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as fou…
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li
We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different poli…
Recurrent Reinforcement Learning: A Hybrid Approach
Xiujun Li, Lihong Li, Jianfeng Gao +4
Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states. It is in general very challenging to construct and…
The Online Coupon-Collector Problem and Its Application to Lifelong Reinforcement Learning
Emma Brunskill, Lihong Li
Transferring knowledge across a sequence of related tasks is an important challenge in reinforcement learning (RL). Despite much encouraging empirical evidence, there has been litt…
On the Prior Sensitivity of Thompson Sampling
Che-Yu Liu, Lihong Li
The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties. One important benefit of the algo…
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives
Shipra Agrawal, Nikhil R. Devanur, Lihong Li
We consider a contextual version of multi-armed bandit problem with global knapsack constraints. In each round, the outcome of pulling an arm is a scalar reward and a resource cons…