Human beings, even small children, quickly become adept at figuring out how to use applications on their mobile devices. Learning to use a new app is often achieved via trial-and-error, accelerated by transfer of knowledge from past experiences with like apps. The prospect of building a smarter smartphone - one that can learn how to achieve tasks using mobile apps - is tantalizing. In this paper we explore the use of Reinforcement Learning (RL) with the goal of advancing this aspiration. We introduce an RL-based framework for learning to accomplish tasks in mobile apps. RL agents are provided with states derived from the underlying representation of on-screen elements, and rewards that are based on progress made in the task. Agents can interact with screen elements by tapping or typing. Our experimental results, over a number of mobile apps, show that RL agents can learn to accomplish multi-step tasks, as well as achieve modest generalization across different apps. More generally, we develop a platform which addresses several engineering challenges to enable an effective RL training environment. Our AppBuddy platform is compatible with OpenAI Gym and includes a suite of mobile apps and benchmark tasks that supports a diversity of RL research in the mobile app setting.

翻译：人类,甚至幼儿,很快就学会了如何在移动设备上使用应用程序。 学习使用新应用程序往往通过试镜和感官方式实现,而通过以类似应用程序传授以往经验的知识而加速。 建立智能智能手机的前景 — — 一个可以学习如何使用移动应用程序完成任务的智能手机 — 正在酝酿之中。 在本文件中,我们探索如何利用强化学习(RL)来推进这一愿望。 我们引入了一个基于RL的框架来学习如何在移动应用程序中完成任务。 我们引入了一个基于RL的学习框架,以便学习如何在移动应用程序中完成任务。 向RL代理提供了来自屏幕元素基本代表的国家,以及基于任务进展的奖励。 代理商可以通过窃听或打字与屏幕元素进行互动。 我们的实验结果,超过许多移动应用程序,表明RL代理商可以学会完成多步任务,并且在不同应用程序中实现适度的普及。 更一般地说,我们开发了一个平台,用来解决一些工程挑战,以便能够在移动应用程序中完成有效的培训环境。 我们的AppBuddy平台与 Opuddy 平台与OpAI Gym 以及一个移动应用程序的套支持多样性的移动应用和基准任务。