92 lines
3.6 KiB
Markdown
92 lines
3.6 KiB
Markdown
# OSWorld: Open-Ended Tasks in Real Computer Environments
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<p align="center">
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<img src="desktop_env/assets/icon.jpg" alt="Logo" width="80px">
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<br>
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<b>SLOGAN</b>
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</p>
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<p align="center">
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<a href="">Website</a> •
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<a href="">Paper</a>
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</p>
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![Overview]()
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## Updates
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- 2024-03-01: We released our [paper](), [environment code](), [dataset](), and [project page](). Check it out!
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## Install
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1. Install VMWare and configure `vmrun` command:
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Please refer to [guidance](https://docs.google.com/document/d/1KBdeZwmZs2Vi_Wsnngb3Wf1-RiwMMpXTftwMqP2Ztak/edit#heading=h.uh0x0tkl7fuw)
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2. Install the environment package, download the examples and the virtual machine image.
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For x86_64 Linux or Windows, you can install the environment package and download the examples and the virtual machine image by running the following commands:
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```bash
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pip install desktop-env
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gdown xxxx
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vmrun -T ws start "Ubuntu/Ubuntu.vmx" nogui
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vmrun -T ws snapshot "Ubuntu/Ubuntu.vmx" "init_state"
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```
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## Quick Start
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Run the following minimal example to interact with the environment:
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```python
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import json
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from desktop_env.envs.desktop_env import DesktopEnv
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with open("evaluation_examples/examples/gimp/f723c744-e62c-4ae6-98d1-750d3cd7d79d.json", "r", encoding="utf-8") as f:
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example = json.load(f)
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env = DesktopEnv(
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path_to_vm=r"path_to_vm",
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action_space="computer_13",
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task_config=example
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)
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observation = env.reset()
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observation, reward, done, info = env.step({"action_type": "CLICK", "parameters": {"button": "right", "num_clicks": 1}})
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```
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## Annotation Tool Usage
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We provide an annotation tool to help you annotate the examples.
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## Agent Usage
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We provide a simple agent to interact with the environment. You can use it as a starting point to build your own agent.
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## Road map of infra (Proposed)
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- [x] Explore VMWare, and whether it can be connected and control through mouse package
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- [x] Explore Windows and MacOS, whether it can be installed
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- MacOS is closed source and cannot be legally installed
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- Windows is available legally and can be installed
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- [x] Build gym-like python interface for controlling the VM
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- [x] Recording of actions (mouse movement, click, keyboard) for humans to annotate, and we can replay it and compress it
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- [x] Build a simple task, e.g. open a browser, open a website, click on a button, and close the browser
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- [x] Set up a pipeline and build agents implementation (zero-shot) for the task
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- [x] Start to design on which tasks inside the DesktopENv to focus on, start to wrap up the environment to be public
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- [x] Start to annotate the examples for ~~training~~ and testing
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- [x] Error handling during file passing and file opening, etc.
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- [x] Add accessibility tree from the OS into the observation space
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- [x] Add pre-process and post-process action support for benchmarking setup and evaluation
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- [ ] Multiprocess support, this can enable the reinforcement learning to be more efficient
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- [ ] Experiment logging and visualization system
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- [ ] Add more tasks, maybe scale to 300 for v1.0.0, and create a dynamic leaderboard
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## Road map of benchmark, tools and resources (Proposed)
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- [ ] Improve the annotation tool base on DuckTrack, make it more robust which align on accessibility tree
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- [ ] Annotate the steps of doing the task
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- [ ] Build a website for the project
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- [ ] Crawl all resources we explored from the internet, and make it easy to access
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- [ ] Set up ways for community to contribute new examples
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## Citation
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If you find this environment useful, please consider citing our work:
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```
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@article{DesktopEnv,
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title={},
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author={},
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journal={arXiv preprint arXiv:xxxx.xxxx},
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year={2024}
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}
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``` |