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In recent years, trends towards studying simulated games have gained momentumin the fields of artificial intelligence, cognitive science, psychology, andneuroscience. The intersections of these fields have also grown recently, asresearchers increasing study such games using both artificial agents and humanor animal subjects. However, implementing games can be a time-consumingendeavor and may require a researcher to grapple with complex codebases thatare not easily customized. Furthermore, interdisciplinary researchers studyingsome combination of artificial intelligence, human psychology, and animalneurophysiology face additional challenges, because existing platforms aredesigned for only one of these domains. Here we introduce ModularObject-Oriented Games, a Python task framework that is lightweight, flexible,customizable, and designed for use by machine learning, psychology, andneurophysiology researchers.

Modular Object-Oriented Games: A Task Framework for Reinforcement Learning, Psychology, and Neuroscience