Papers › AndroidEnv: A Reinforcement Learning Platform for Android

AndroidEnv: A Reinforcement Learning Platform for Android

27 May 2021arXiv:2105.13231archive 2025-07-28

Daniel Toyama, Philippe Hamel, Anita Gergely, Gheorghe Comanici, Amelia Glaese, Zafarali Ahmed, Tyler Jackson, Shibl Mourad, Doina Precup

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.

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deepmind/android_env officialmentioned in papermentioned on GitHubApache-2.0 report
google-deepmind/android_env mentioned on GitHubApache-2.0 report
yizhangliu/android_env_for_windows mentioned on GitHubApache-2.0 report

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build_tree_from_dumpsys_output deepmind/android_env/android_env/components/app_screen_checker.py official repository unverified Apache-2.0 (permissive) · 0258648ec8cfc596 · report
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Reinforcement LearningReinforcement Learning (RL)reinforcement-learning

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