| Name | mobile-env-rl JSON |
| Version |
3.6
JSON |
| download |
| home_page | https://github.com/X-LANCE/Mobile-Env |
| Summary | A Universal Platform for Training and Evaluation of Mobile Interaction |
| upload_time | 2024-04-30 07:08:06 |
| maintainer | None |
| docs_url | None |
| author | Danyang Zhang @X-Lance |
| requires_python | >=3.7 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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| keywords |
infoui
interaction
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!-- vimc: call SyntaxRange#Include('```sh', '```', 'sh', 'NonText'): -->
<!-- vimc: call SyntaxRange#Include('```bibtex', '```', 'bib', 'NonText'): -->
## NEWS!!
* (2024-04-30 v3.6)
* Updated function to load a remote simulator to enable providing the remote
resources with a different path with the path of the local task definition
file.
* Updated task template toolkit, added new slot modifiers and sytaxes for
task config file.
* Fixed known bugs.
* (2023-12-18 v3.5)
* Owing to the long time delay of VH check and screenshot check, we updated
the mechanism of managing the check time. By this way, the requirement of
sufficient check for the episode events and the resulted long delay can be
balanced.
* Added multiple rating methods to `ResponseEvent`: regex matching, fuzzy
matching, and vector encoding matching.
* Improved `VhIoWrapper` and `TapActionWrapper`. Added support to `SCROLL`
and `TYPE` to `TapActionWrapper`.
* Optimized `RemoteSimulator`. In order to reduce the delay of network
transfering, enabled action batch to send and execute a group of actions
and enabled resizing the image before and after transferring to shrink the
transferred data.
* Merged annotation tool to the main branch. The original annotation-tool
branch is deprecated.
* Added support to `ResponseEvent` to annotation tool.
* Supplemented several commandline options to annotation-tool.
For more details, please see our [Change Log](Changelog) and Documents.
* (2023-10-31 v3.0) Migrated VH node specification from the original VH path to
Mobile-Env-customized CSS selector (me-selector) and added repeatability
control to EventSlots. Repeatability control for EventSlots may be useful to
prevent repetitive triggering of an `OR`-type virtual event combining
multiple types of event sources.
Please see our [Change Log](Changelog) and
[Document](docs/task-definition-en.md).
* (2023-09-21 v2.1) Added *REMOTE SIMULATOR* to solve the problem that
hardware-based acceleration for virtualization is not enabled on many GPU
clusters
Please see our [Change Log](Changelog) and [Document](docs/env-usage-en.md).
* (2023-06-30 v2.0) New type of event "response to human user" (RHU,
`ResponseEvent`). Now enables the agent to generate response to human user
and parses episode signales from it. This will enable interaction tasks like
question-answering, retrieval, *etc*.
Please see our [Change Log](Changelog), [Usage Document](docs/env-usage-en.md),
and [Task Definition Document](docs/task-definition-en.md).
# Mobile-Env: An Evaluation Platform and Benchmark for Interactive Agents in LLM Era
Mobile-Env is a interaction platform for evaluation and training of the
interaction of information user interface (InfoUI). Our paper is available at
[arXiv](https://arxiv.org/abs/2305.08144).
Mobile-Env is developed based on
[AndroidEnv](https://github.com/deepmind/android_env). The agent can take the
screenshot and the view hierarchy (disabled defaultly for the long latency) as
the observation and take a touch or type a token as the action to interact with
the Android apps. Several episode signals like step instructions, rewards, or
the episode end will be informed during interaction at some crucial steps. A
so-called crucial step may be opening a target page, srolling to a correct
area, *etc*. and is depending on the specific [task
definition](docs/task-definition-en.md).
The proposed WikiHow task set is available at the [Hugging Face
Platform](https://huggingface.co/datasets/zdy023/WikiHow-taskset).
* [Documents in Chinese (中文文档)](README-zh.md)
## Index
* [Evaluating and Training Agents on Mobile-Env](docs/env-usage-en.md)
* [Extending a New Environment (App) or a New Task Based on
Mobile-Env](docs/task-definition-en.md)
* [Certificate Pinning Problem & Solutions](docs/dynamic-app-en.md)
* [Miscellaneous Auxiliary Tools](docs/other-tools-en.md)
## Platform Features
Mobile-Env is a flexible, adaptable, and easily-extendable platform for InfoUI
interaction with the following features:
* Both screenshot and view hierarchy are provided as the observation. The touch
and token typing are provided as the action. Wrappers are also supported to
customize the observation and action spaces. Thus, both visual-based and
text-based agents, both agents with continuous action space and discrete
action space, can be evaluated on Mobile-Env.
* New tasks can be easily extended through task definition files.
* Multiple sources are enabled to parse the task events from the operating
system: screen text, screen icon, view hierarchy, and the system log, which
makes Mobile-Env capable of adapting to most real-world apps without
dedicated development. (Screen text and screen icon will be enabled with an
external OCR tool and icon recognition tool. Currently, a wrapper of
[EasyOCR](https://github.com/JaidedAI/EasyOCR) is integrated in the platform
and can be enabled directly. An intergrated icon model will be embedded soon
as well.)
## Getting Started
### Installation
Install from PyPI:
```sh
pip insall mobile-env-rl
```
or clone the repository and build locally.
```sh
git clone https://github.com/X-LANCE/Mobile-Env
cd Mobile-Env
pip install .
```
Several [Docker images](https://hub.docker.com/r/zdy023/mobile-env-rl) with
well-configured Android AVD are also available.
### Load and Run Mobile-Env for Evaluation or Training
Before loading the Mobile-Env environment, you will need to set up an [Android
Emulator](https://developer.android.com/about) device. Then you can load the
environment with some existing task definitions and start your experiments. A
detailed guidance is provided in [Evaluating and Traning Agents on
Mobile-Env](docs/env-usage-en.md). Several examples with a random agent or a
human agent is also provided under `examples`.
### Extend a New Environment or a New Task
To extend a new environment for Mobile-Env, the environment designer needs to
prepare the app package and ensure that the package manages to launch and run
on some versions of Android Emulator. If the app requires varying online data,
the necessary data should be crawled and dumped and then be replayed for a
consistent evaluation. In such case, the designer is supposed to validate the
certain effectiveness of [certificate unpinning plan](docs/dynamic-app-en.md)
for the package. As regards to extend new tasks, task definition files are
just required. Detailed instructions can be found in [Extending a New
Environment (App) or a New Task Based on
Mobile-Env](docs/task-definition-en.md).
Several demo task definitions are provided under `demos`. Three of them are
migrated from AndroidEnv:
* `classic_2048.m.textproto` - [Classic 2048
game](https://github.com/google-deepmind/android_env/blob/main/docs/example_tasks.md#classic-2048).
* `accessibility_forwarder_clock_set_timer.m.textproto` - A simple task
requiring the agent to [reset a running
timer](https://github.com/google-deepmind/android_env/blob/main/docs/example_tasks.md#accessibility-forwarder).
* `systemui_egg_land_default.m.textproto` - [Flappy
Droid](https://github.com/google-deepmind/android_env/blob/main/docs/example_tasks.md#flappydroid).
An open-sourced implementation of classic game, Flappy Bird.
Another one, `openmoneybox.add_billings.textproto` is defined upon an
open-sourced billing app,
[OpenMoneyBox](https://f-droid.org/en/packages/com.igisw.openmoneybox/).
Details are referred to in the task definition files.
### Miscellaneous Auxiliary Tools
We also developed an annotation tool for the human demonstrations, and a suite
of template tool to auto-generate task definitions according to templates and
to combine multiple task definitions to form a multi-step task. The details are
referred to in [Miscellaneous Auxiliary Tools](docs/other-tools-en.md).
### Reference Time-Consuming and Memory Usage o Mobile-Env
The data are measured under the configuration below:
* OS and hardware:
* Operating System: Manjaro 23.1.0 Vulcan
* Kernel Version: x86\_64 Linux 6.1.64-1-MANJARO
* CPU: Intel Core i7-10700 @ 16x 4.8GHz
* GPU: NVIDIA GeForce RTX 3090
* RAM: 64 GB
* KVM acceleration enabled
* Android development tools
* Android emulator version 32.1.14.0
* Android platform tools 34.0.4
* libvert 1:9.9.0
* Python & packages
* Python 3.8.16
* EasyOCR 1.7.2
* sentence-transformers 2.2.2
* Android Virtual Device
* Device type: Pixel 2
* API version: API 30
* OS Variant: Google APIs
* CPU cores: 4
* Memory: 8 GB
* Screen size: 1080×1920
| Item | Avg Time | Time Std Dev |
|:-------------------------------------------------------:|:--------:|:------------:|
| `TOUCH` action | 410.50µs | 64.71µs |
| `LIFT` action | 412.30µs | 84.18µs |
| `TEXT` action | 1.30s | 0.28s |
| screenshot capturing | 19.94ms | 21.47ms |
| invocation of Sentence Transformer（all-MiniLM-L12-v2） | 8.51ms | 0.17ms |
| VH capturing | 2.53s | 1.90s |
| invocation of EasyOCR | 0.44s | 0.08s |
When only an [app of WikiHow
2.9.6](https://apkcombo.com/zh/wikihow-how-to-do-anything/com.wikihow.wikihowapp/download/apk)
is running, the Android emulator occupies 6,031 MiB of virtual memory and 3,444
MiB of residual memory.
## About
This library is developed and maintained by [SJTU
X-Lance](https://x-lance.sjtu.edu.cn/en). The corresponding paper is available
at <https://arxiv.org/abs/2305.08144>.
If you find Mobile-Env useful in your research, you can cite the project using
the following BibTeX:
```bibtex
@article{DanyangZhang2023_MobileEnv,
title = {{Mobile-Env}: An Evaluation Platform and Benchmark for LLM-GUI Interaction},
author = {Danyang Zhang and
Hongshen Xu and
Zihan Zhao and
Lu Chen and
Ruisheng Cao and
Kai Yu},
journal = {CoRR},
volume = {abs/2305.08144},
year = {2023},
url = {https://arxiv.org/abs/2305.08144},
eprinttype = {arXiv},
eprint = {2305.08144},
}
```
Raw data
{
"_id": null,
"home_page": "https://github.com/X-LANCE/Mobile-Env",
"name": "mobile-env-rl",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "InfoUI interaction",
"author": "Danyang Zhang @X-Lance",
"author_email": "\"Danyang Zhang @X-Lance\" <zdy004007@126.com>",
"download_url": "https://files.pythonhosted.org/packages/48/2d/7dfc15ba53cde589b901bf39a520ab1ab117fb93fb69c39ae6e93ee876dc/mobile_env_rl-3.6.tar.gz",
"platform": null,
"description": "<!-- vimc: call SyntaxRange#Include('```sh', '```', 'sh', 'NonText'): -->\n<!-- vimc: call SyntaxRange#Include('```bibtex', '```', 'bib', 'NonText'): -->\n\n## NEWS!!\n\n* (2024-04-30 v3.6)\n * Updated function to load a remote simulator to enable providing the remote\n resources with a different path with the path of the local task definition\n file.\n * Updated task template toolkit, added new slot modifiers and sytaxes for\n task config file.\n * Fixed known bugs.\n\n* (2023-12-18 v3.5)\n * Owing to the long time delay of VH check and screenshot check, we updated\n the mechanism of managing the check time. By this way, the requirement of\n sufficient check for the episode events and the resulted long delay can be\n balanced.\n * Added multiple rating methods to `ResponseEvent`: regex matching, fuzzy\n matching, and vector encoding matching.\n * Improved `VhIoWrapper` and `TapActionWrapper`. Added support to `SCROLL`\n and `TYPE` to `TapActionWrapper`.\n * Optimized `RemoteSimulator`. In order to reduce the delay of network\n transfering, enabled action batch to send and execute a group of actions\n and enabled resizing the image before and after transferring to shrink the\n transferred data.\n * Merged annotation tool to the main branch. The original annotation-tool\n branch is deprecated.\n * Added support to `ResponseEvent` to annotation tool.\n * Supplemented several commandline options to annotation-tool.\n\nFor more details, please see our [Change Log](Changelog) and Documents.\n\n* (2023-10-31 v3.0) Migrated VH node specification from the original VH path to\n Mobile-Env-customized CSS selector (me-selector) and added repeatability\n control to EventSlots. Repeatability control for EventSlots may be useful to\n prevent repetitive triggering of an `OR`-type virtual event combining\n multiple types of event sources.\n\nPlease see our [Change Log](Changelog) and\n[Document](docs/task-definition-en.md).\n\n* (2023-09-21 v2.1) Added *REMOTE SIMULATOR* to solve the problem that\n hardware-based acceleration for virtualization is not enabled on many GPU\n clusters\n\nPlease see our [Change Log](Changelog) and [Document](docs/env-usage-en.md).\n\n* (2023-06-30 v2.0) New type of event \"response to human user\" (RHU,\n `ResponseEvent`). Now enables the agent to generate response to human user\n and parses episode signales from it. This will enable interaction tasks like\n question-answering, retrieval, *etc*.\n\nPlease see our [Change Log](Changelog), [Usage Document](docs/env-usage-en.md),\nand [Task Definition Document](docs/task-definition-en.md).\n\n# Mobile-Env: An Evaluation Platform and Benchmark for Interactive Agents in LLM Era \n\nMobile-Env is a interaction platform for evaluation and training of the\ninteraction of information user interface (InfoUI). Our paper is available at\n[arXiv](https://arxiv.org/abs/2305.08144).\n\nMobile-Env is developed based on\n[AndroidEnv](https://github.com/deepmind/android_env). The agent can take the\nscreenshot and the view hierarchy (disabled defaultly for the long latency) as\nthe observation and take a touch or type a token as the action to interact with\nthe Android apps. Several episode signals like step instructions, rewards, or\nthe episode end will be informed during interaction at some crucial steps. A\nso-called crucial step may be opening a target page, srolling to a correct\narea, *etc*. and is depending on the specific [task\ndefinition](docs/task-definition-en.md).\n\nThe proposed WikiHow task set is available at the [Hugging Face\nPlatform](https://huggingface.co/datasets/zdy023/WikiHow-taskset).\n\n* [Documents in Chinese (\u4e2d\u6587\u6587\u6863)](README-zh.md)\n\n## Index\n\n* [Evaluating and Training Agents on Mobile-Env](docs/env-usage-en.md)\n* [Extending a New Environment (App) or a New Task Based on\n Mobile-Env](docs/task-definition-en.md)\n* [Certificate Pinning Problem & Solutions](docs/dynamic-app-en.md)\n* [Miscellaneous Auxiliary Tools](docs/other-tools-en.md)\n\n## Platform Features\n\nMobile-Env is a flexible, adaptable, and easily-extendable platform for InfoUI\ninteraction with the following features:\n\n* Both screenshot and view hierarchy are provided as the observation. The touch\n and token typing are provided as the action. Wrappers are also supported to\n customize the observation and action spaces. Thus, both visual-based and\n text-based agents, both agents with continuous action space and discrete\n action space, can be evaluated on Mobile-Env.\n* New tasks can be easily extended through task definition files.\n* Multiple sources are enabled to parse the task events from the operating\n system: screen text, screen icon, view hierarchy, and the system log, which\n makes Mobile-Env capable of adapting to most real-world apps without\n dedicated development. (Screen text and screen icon will be enabled with an\n external OCR tool and icon recognition tool. Currently, a wrapper of\n [EasyOCR](https://github.com/JaidedAI/EasyOCR) is integrated in the platform\n and can be enabled directly. An intergrated icon model will be embedded soon\n as well.)\n\n## Getting Started\n\n### Installation\n\nInstall from PyPI:\n\n```sh\npip insall mobile-env-rl\n```\n\nor clone the repository and build locally.\n\n```sh\ngit clone https://github.com/X-LANCE/Mobile-Env\ncd Mobile-Env\npip install .\n```\n\nSeveral [Docker images](https://hub.docker.com/r/zdy023/mobile-env-rl) with\nwell-configured Android AVD are also available.\n\n### Load and Run Mobile-Env for Evaluation or Training\n\nBefore loading the Mobile-Env environment, you will need to set up an [Android\nEmulator](https://developer.android.com/about) device. Then you can load the\nenvironment with some existing task definitions and start your experiments. A\ndetailed guidance is provided in [Evaluating and Traning Agents on\nMobile-Env](docs/env-usage-en.md). Several examples with a random agent or a\nhuman agent is also provided under `examples`.\n\n### Extend a New Environment or a New Task\n\nTo extend a new environment for Mobile-Env, the environment designer needs to\nprepare the app package and ensure that the package manages to launch and run\non some versions of Android Emulator. If the app requires varying online data,\nthe necessary data should be crawled and dumped and then be replayed for a\nconsistent evaluation. In such case, the designer is supposed to validate the\ncertain effectiveness of [certificate unpinning plan](docs/dynamic-app-en.md)\nfor the package. As regards to extend new tasks, task definition files are\njust required. Detailed instructions can be found in [Extending a New\nEnvironment (App) or a New Task Based on\nMobile-Env](docs/task-definition-en.md).\n\nSeveral demo task definitions are provided under `demos`. Three of them are\nmigrated from AndroidEnv:\n\n* `classic_2048.m.textproto` - [Classic 2048\n game](https://github.com/google-deepmind/android_env/blob/main/docs/example_tasks.md#classic-2048).\n* `accessibility_forwarder_clock_set_timer.m.textproto` - A simple task\n requiring the agent to [reset a running\n timer](https://github.com/google-deepmind/android_env/blob/main/docs/example_tasks.md#accessibility-forwarder).\n* `systemui_egg_land_default.m.textproto` - [Flappy\n Droid](https://github.com/google-deepmind/android_env/blob/main/docs/example_tasks.md#flappydroid).\n An open-sourced implementation of classic game, Flappy Bird.\n\nAnother one, `openmoneybox.add_billings.textproto` is defined upon an\nopen-sourced billing app,\n[OpenMoneyBox](https://f-droid.org/en/packages/com.igisw.openmoneybox/).\nDetails are referred to in the task definition files.\n\n### Miscellaneous Auxiliary Tools\n\nWe also developed an annotation tool for the human demonstrations, and a suite\nof template tool to auto-generate task definitions according to templates and\nto combine multiple task definitions to form a multi-step task. The details are\nreferred to in [Miscellaneous Auxiliary Tools](docs/other-tools-en.md).\n\n### Reference Time-Consuming and Memory Usage o Mobile-Env\n\nThe data are measured under the configuration below:\n\n* OS and hardware:\n * Operating System: Manjaro 23.1.0 Vulcan\n * Kernel Version: x86\\_64 Linux 6.1.64-1-MANJARO\n * CPU: Intel Core i7-10700 @ 16x 4.8GHz\n * GPU: NVIDIA GeForce RTX 3090\n * RAM: 64 GB\n * KVM acceleration enabled\n* Android development tools\n * Android emulator version 32.1.14.0\n * Android platform tools 34.0.4\n * libvert 1:9.9.0\n* Python & packages\n * Python 3.8.16\n * EasyOCR 1.7.2\n * sentence-transformers 2.2.2\n* Android Virtual Device\n * Device type: Pixel 2\n * API version: API 30\n * OS Variant: Google APIs\n * CPU cores: 4\n * Memory: 8 GB\n * Screen size: 1080\u00d71920\n\n| Item | Avg Time | Time Std Dev |\n|:-------------------------------------------------------:|:--------:|:------------:|\n| `TOUCH` action | 410.50\u00b5s | 64.71\u00b5s |\n| `LIFT` action | 412.30\u00b5s | 84.18\u00b5s |\n| `TEXT` action | 1.30s | 0.28s |\n| screenshot capturing | 19.94ms | 21.47ms |\n| invocation of Sentence Transformer\uff08all-MiniLM-L12-v2\uff09 | 8.51ms | 0.17ms |\n| VH capturing | 2.53s | 1.90s |\n| invocation of EasyOCR | 0.44s | 0.08s |\n\nWhen only an [app of WikiHow\n2.9.6](https://apkcombo.com/zh/wikihow-how-to-do-anything/com.wikihow.wikihowapp/download/apk)\nis running, the Android emulator occupies 6,031 MiB of virtual memory and 3,444\nMiB of residual memory.\n\n## About\n\nThis library is developed and maintained by [SJTU\nX-Lance](https://x-lance.sjtu.edu.cn/en). The corresponding paper is available\nat <https://arxiv.org/abs/2305.08144>.\n\nIf you find Mobile-Env useful in your research, you can cite the project using\nthe following BibTeX:\n\n```bibtex\n@article{DanyangZhang2023_MobileEnv,\n title = {{Mobile-Env}: An Evaluation Platform and Benchmark for LLM-GUI Interaction},\n author = {Danyang Zhang and\n Hongshen Xu and\n Zihan Zhao and\n Lu Chen and\n Ruisheng Cao and\n Kai Yu},\n journal = {CoRR},\n volume = {abs/2305.08144},\n year = {2023},\n url = {https://arxiv.org/abs/2305.08144},\n eprinttype = {arXiv},\n eprint = {2305.08144},\n}\n```\n",
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