  We address the relative paucity of empirical testing of learning algorithms
(of any type) by introducing a new public-domain, Modular, Optimal Learning
Testing Environment (MOLTE) for Bayesian ranking and selection problem,
stochastic bandits or sequential experimental design problems. The Matlab-based
simulator allows the comparison of a number of learning policies (represented
as a series of .m modules) in the context of a wide range of problems (each
represented in its own .m module) which makes it easy to add new algorithms and
new test problems. State-of-the-art policies and various problem classes are
provided in the package. The choice of problems and policies is guided through
a spreadsheet-based interface. Different graphical metrics are included. MOLTE
is designed to be compatible with parallel computing to scale up from local
desktop to clusters and clouds. We offer MOLTE as an easy-to-use tool for the
research community that will make it possible to perform much more
comprehensive testing, spanning a broader selection of algorithms and test
problems. We demonstrate the capabilities of MOLTE through a series of
comparisons of policies on a starter library of test problems. We also address
the problem of tuning and constructing priors that have been largely overlooked
in optimal learning literature. We envision MOLTE as a modest spur to provide
researchers an easy environment to study interesting questions involved in
optimal learning.
