Doubly Robust Policy Evaluation and Optimization

Doubly Robust Policy Evaluation and Learning

We study decision making in environments where the reward is only partially observed, but can be modeled as a function of an action and an observed context. This setting, known as contextual bandits, encompasses a wide variety of applications including health-care policy and Internet advertising. A central task is evaluation of a new policy given historic data consisting of contexts, actions an...

Large-scale Validation of Counterfactual Learning Methods: A Test-Bed

The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news. Recent approaches for off-policy evaluation and learning in these settings appear promising [1, 2]. With this paper, we provide real-world data and a standardized test-be...

Robust Optimization and Confidence Interval DEA for Efficiency Evaluation with Intervals Case Study: Evaluating CRM Units in a Call Center in Tehran

Lyapunov-Based Robust Power Controllers for a Doubly Fed Induction Generator

In this work, a robust nonlinear control technique of a doubly fed induction generator (DFIG) intended for wind energy systems has been proposed. The principal idea in this article is to decouple the active and reactive power of the DFIG with high robustness using the backstepping strategy. The principle of this control method is based on the Lyapunov function, in order to guarantee the global ...

More Robust Doubly Robust Off-policy Evaluation

We study the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of a policy from the data generated by another policy(ies). In particular, we focus on the doubly robust (DR) estimators that consist of an importance sampling (IS) component and a performance model, and utilize the low (or zero) bias of IS and low variance of the mo...