Deep learning has provided new ways of manipulating, processing and analyzing data. It
sometimes may achieve results comparable to, or surpassing human expert performance …

Importance Sampling (IS) is a widely used building block for a large variety of off-policy
estimation and learning algorithms. However, empirical and theoretical studies have …

In reinforcement learning, importance sampling is a widely used method for evaluating an
expectation under the distribution of data of one policy when the data has in fact been …

Bandit and reinforcement learning (RL) problems can often be framed as optimization
problems where the goal is to maximize average performance while having access only to …

This paper considers how to complement offline reinforcement learning (RL) data with
additional data collection for the task of policy evaluation. In policy evaluation, the task is to …

This work serves as a review of our experience applying off-policy techniques to train and
evaluate a contextual bandit model powering a troubleshooting notification in a chatbot …

Most of existing advantage function estimation methods in reinforcement learning suffer from
the problem of high variance, which scales unfavorably with the time horizon. To address …

Importance Sampling (IS) is a widely used building block for a large variety of off-policy
estimation and learning algorithms. However, empirical and theoretical studies have …

Off-policy methods are the basis of a large number of effective Policy Optimization (PO)
algorithms. In this setting, Importance Sampling (IS) is typically employed as a what-if …

In recent years, reinforcement learning (RL) has been increasingly successful at solving
complex tasks. Despite these successes, one of the fundamental challenges is that many RL …