홈 › Papers

AndroidEnv: A Reinforcement Learning Platform for Android

2021-05-27 · 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.

📄 PDF Abstract BibTeX arXiv:2105.13231

Code (3)

deepmind/android_env 공식 구현

google-deepmind/android_env

yizhangliu/android_env_for_windows

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning

2022-04-21 · Gheorghe Comanici, Amelia Glaese, Anita Gergely, Daniel Toyama 외

Hierarchical Reinforcement Learning (HRL) allows interactive agents to decompose complex problems into a hierarchy of sub-tasks. Higher-level tasks can invoke the solutions of lower-level tasks as if they were primitive …

Deep Reinforcement Learning Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Handroid: Bridging Dexterous Hand and Humanoid

2026-07-17 · Ruogu Li, Chenyang Ma, Sikai Li, Zhenyu Wei 외 arxiv

Dexterous hands and humanoid robots are typically developed as distinct embodiments: the former enable contact-rich manipulation at the object scale, whereas the latter provide mobility and whole-body interaction in huma…

Analysis of Bayesian Classification based Approaches for Android Malware Detection

2016-08-20 · Suleiman Y. Yerima, Sakir Sezer, Gavin McWilliams

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting …

Android Malware Detection Classification General Classification Malware Detection

Android Malware Detection Using Machine Learning on Image Patterns

2019-01-27 · 2018 Cyber Resilience Conference (CRC) 2019 1 · Fauzi Mohd Darus, Noor Azurati Ahmad Salleh, Aswami Fadillah Mohd Ariffin

Android platform has been targeted by cyber-criminals due to the increase number of Android users in 2017. More than 8,000 Android malware were identified everyday making it is difficult for the malware analyst to detect…

Android Malware Detection Malware Detection

FedKit: Enabling Cross-Platform Federated Learning for Android and iOS

2024-02-16 · Sichang He, Beilong Tang, Boyan Zhang, Jiaoqi Shao 외

We present FedKit, a federated learning (FL) system tailored for cross-platform FL research on Android and iOS devices. FedKit pipelines cross-platform FL development by enabling model conversion, hardware-accelerated tr…

Federated Learning