Contextual: Multi-Armed Bandits in R
R package facilitating the simulation and evaluation of context-free and contextual Multi-Armed Bandit policies.
The package has been developed to:
- Introduce a wider audience to contextual bandit policies' advanced sequential decision strategies.
- Ease the implementation, evaluation and dissemination of both existing and new contextual Multi-Armed Bandit policies.
To install R6 from CRAN:
To install the development version (requires the devtools package):
See the demo directory for practical examples and replications of both synthetic and offline (contextual) bandit policy evaluations, such as for instance:
A basic introduction to multi-armed bandit problems in general, and the use of the R package contextual in specific can be found in the this paper.
Overview of core classes
Policies and Bandits
Overview of contextual's growing library of contextual and context-free bandit policies:
|CMAB Naive Epsilon-Greedy
LinUCB (General, Disjoint, Hybrid)
|Lock-in Feedback (LiF)
Overview of contextual's bandit library:
Robin van Emden: author, maintainer* Maurits Kaptein: supervisor*
If you encounter a clear bug, please file a minimal reproducible example on GitHub.