45 citations · 94 across the 7 of their papers we have counts for
5 papers · 1 filter
Directed Exploration for Reinforcement Learning
Zhaohan Daniel Guo, Emma Brunskill
Efficient exploration is necessary to achieve good sample efficiency for reinforcement learning in general. From small, tabular settings such as gridworlds to large, continuous and…
Sample Efficient Feature Selection for Factored MDPs
Zhaohan Daniel Guo, Emma Brunskill
In reinforcement learning, the state of the real world is often represented by feature vectors. However, not all of the features may be pertinent for solving the current task. We p…
A PAC RL Algorithm for Episodic POMDPs
Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill
Many interesting real world domains involve reinforcement learning (RL) in partially observable environments. Efficient learning in such domains is important, but existing sample c…
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users
Li Zhou, Emma Brunskill
Personalized recommendations for new users, also known as the cold-start problem, can be formulated as a contextual bandit problem. Existing contextual bandit algorithms generally…
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
Philip S. Thomas, Emma Brunskill
In this paper we present a new way of predicting the performance of a reinforcement learning policy given historical data that may have been generated by a different policy. The ab…