deepmind / android_env

AndroidEnv is a Python library that exposes an Android device as a Reinforcement Learning (RL) environment. The library provides a flexible platform for defining custom tasks on top of the Android Operating System, including any Android application. Agents interact with the device through a universal action interface - the touchscreen - by sending localized touch and lift events to the system. The library processes these events and returns pixel observations and rewards as provided by specific task definitions. For example, rewards might be given for events such as successfully scrolling down a page, sending an email, or achieving some score in a game, depending on the research purpose and how the user configures the task.

There are a number of aspects that make AndroidEnv a challenging yet suitable environment for Reinforcement Learning research:

Allowing agents to interact with a system used daily by billions of users around the world, AndroidEnv offers a platform for RL agents to navigate, learn tasks and have direct impact in real-world contexts. The environment wraps a simulated Android device, which runs independently from the environment, completely unaltered, and works in exactly the same way as the devices that humans use, exposing exactly the same features and services.

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