MOLTE: a Modular Optimal Learning Testing Environment
Yingfei Wang, Warren Powell

TL;DR
MOLTE is a modular, Matlab-based testing environment designed to facilitate comprehensive empirical evaluation of Bayesian learning algorithms across diverse problems, supporting easy comparison, customization, and scalability.
Contribution
It introduces a flexible, open-source platform for testing and comparing various learning policies on a wide range of problems, enhancing research capabilities in optimal learning.
Findings
Demonstrated MOLTE's ability to compare policies effectively.
Showcased the platform's scalability with parallel computing.
Addressed prior gaps in tuning and prior construction in the literature.
Abstract
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…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Machine Learning and Algorithms · Machine Learning and Data Classification
