contextual: Evaluating Contextual Multi-Armed Bandit Problems in R
This provides a practical tool for researchers and practitioners working on sequential decision problems in fields like online advertising and personalized medicine, though it is incremental as it builds on existing bandit algorithms.
The paper addresses the lack of standardized tools for simulating and comparing contextual bandit algorithms by introducing the R package 'contextual', which provides a user-friendly, extensible framework for parallelized evaluation of policies through simulation and offline analysis.
Over the past decade, contextual bandit algorithms have been gaining in popularity due to their effectiveness and flexibility in solving sequential decision problems---from online advertising and finance to clinical trial design and personalized medicine. At the same time, there are, as of yet, surprisingly few options that enable researchers and practitioners to simulate and compare the wealth of new and existing bandit algorithms in a standardized way. To help close this gap between analytical research and empirical evaluation the current paper introduces the object-oriented R package "contextual": a user-friendly and, through its object-oriented structure, easily extensible framework that facilitates parallelized comparison of contextual and context-free bandit policies through both simulation and offline analysis.