We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem. AndroidEnv allows RL agents to interact with a wide variety of apps and services commonly used by humans through a universal touchscreen interface. Since agents train on a realistic simulation of an Android device, they have the potential to be deployed on real devices. In this report, we give an overview of the environment, highlighting the significant features it provides for research, and we present an empirical evaluation of some popular reinforcement learning agents on a set of tasks built on this platform.
@article{arxiv.2105.13231,
title = {AndroidEnv: A Reinforcement Learning Platform for Android},
author = {Daniel Toyama and Philippe Hamel and Anita Gergely and Gheorghe Comanici and Amelia Glaese and Zafarali Ahmed and Tyler Jackson and Shibl Mourad and Doina Precup},
journal= {arXiv preprint arXiv:2105.13231},
year = {2021}
}