MOLTE: a Modular Optimal Learning Testing Environment
There has been a long history in the optimal learning literature of proving some sort of bound, supported at times by relatively thin empirical work by comparing a few policies on a small number of randomly generated problems. To this end, we designed MOLTE as a public-domain test environment to facilitate the process of more comprehensive comparisons, on a broader set of test problems and a broader set of policies, so that researchers can more easily draw insights into the behavior of different policies in the context of different problem classes.
The Matlab-based simulator allows the comparison of a number of learning policies (encapsulated 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.
The complete system is available at: http://castlelab.princeton.edu/
A user's manual is available here.
Please also refer to the following paper that demonstrate the capabilities of MOLTE through a series of experimental comparisons of policies on a starter library of test problems:
Y. Wang and W. Powell.
MOLTE: a modular optimal learning testing environment.