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Importance Sampling with Unequal Support
Philip S. Thomas, Emma Brunskill
Importance sampling is often used in machine learning when training and testing data come from different distributions. In this paper we propose a new variant of importance samplin…
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…