AndroidEnv: A Reinforcement Learning Platform for Android
Daniel Toyama Affiliation: Equal contributions Affiliation: DeepMind Philippe Hamel Affiliation: Equal contributions Affiliation: DeepMind Anita Gergely Affiliation: Equal contributions Affiliation: DeepMind Gheorghe Comanici Affiliation: Equal contributions Affiliation: DeepMind Amelia Glaese Affiliation: DeepMind Zafarali Ahmed Affiliation: DeepMind Tyler Jackson Affiliation: DeepMind Shibl Mourad Affiliation: DeepMind Doina Precup Affiliation: DeepMind
Abstract
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.
原文 arXiv:2105.13231;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2105.13231v1