miniwob 1.0
A web interaction benchmark for reinforcement learning.
License: MIT License
Requires Python: >=3.8
Keywords: Reinforcement Learning,game,RL,AI,gymnasium

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The MiniWoB++ (Mini World of Bits++) library contains a collection of over 100 **web interaction environments**,
along with JavaScript and Python interfaces for programmatically interacting with them.
The Python interface follows the [Gymnasium](https://gymnasium.farama.org/) API
and uses [Selenium WebDriver](https://www.selenium.dev/documentation/webdriver/)
to perform actions on the web browser. 

MiniWoB++ is an extension of the
[OpenAI MiniWoB benchmark](http://proceedings.mlr.press/v70/shi17a/shi17a.pdf),
and was introduced in the paper
[Reinforcement Learning on Web Interfaces using Workflow-Guided
Exploration](https://arxiv.org/abs/1802.08802).

The documentation website is at [miniwob.farama.org](https://miniwob.farama.org/).
Development on MiniWoB++ is currently ongoing to bring it up to [Farama Standards](https://farama.org/project_standards.html) for mature projects, and will be maintained long term after this point. See the [Project Roadmap](https://github.com/Farama-Foundation/miniwob-plusplus/issues/58) for more details. If you'd like to help out, you can join our discord server here: <https://discord.gg/PfR7a79FpQ>.

# Installation

MiniWoB++ supports Python 3.8+ on Linux and macOS.

## Installing the MiniWoB++ Library

To install the MiniWoB++ library, use `pip install miniwob`.

## Installing Chrome/Chromium and ChromeDriver

We strongly recommend using Chrome or Chromium as the web browser,
as other browsers may render the environments differently.

The MiniWoB++ Python interface uses [Selenium](https://www.selenium.dev/documentation/webdriver/),
which interacts with the browser via the [WebDriver API](https://w3c.github.io/webdriver/).
Follow one of the
[instruction methods](https://www.selenium.dev/documentation/webdriver/getting_started/install_drivers/)
to install ChromeDriver. The simplest method is to
[download](https://chromedriver.chromium.org/downloads) ChromeDriver with the matching version,
unzip it, and then add the directory containing the `chromedriver` executable to the `PATH` environment variable:

```sh
export PATH=$PATH:/path/to/chromedriver
```

For Chromium, the driver may also be available in a software package; for example, in Debian/Ubuntu:

```sh
sudo apt install chromium-driver
```

# Example Usage

The following code performs a deterministic action on the
[`click-test-2`](http://miniwob.farama.org/environments/click-test-2/) environment. 

```python
import time
import gymnasium
from miniwob.action import ActionTypes

env = gymnasium.make('miniwob/click-test-2-v1', render_mode='human')

# Wrap the code in try-finally to ensure proper cleanup.
try:
  # Start a new episode.
  obs, info = env.reset()
  assert obs["utterance"] == "Click button ONE."
  assert obs["fields"] == (("target", "ONE"),)
  time.sleep(2)       # Only here to let you look at the environment.
  
  # Find the HTML element with text "ONE".
  for element in obs["dom_elements"]:
    if element["text"] == "ONE":
      break

  # Click on the element.
  action = env.unwrapped.create_action(ActionTypes.CLICK_ELEMENT, ref=element["ref"])
  obs, reward, terminated, truncated, info = env.step(action)

  # Check if the action was correct. 
  print(reward)      # Should be around 0.8 since 2 seconds has passed.
  assert terminated is True
  time.sleep(2)

finally:
  env.close()
```

See [the documentation](http://miniwob.farama.org/content/basic_usage/) for more information.

# Environments

The list of the environments that were included in the MiniWoB++ library can be found in the
[documentation](http://miniwob.farama.org/environments/list/).
All environments share the same [observation space](http://miniwob.farama.org/content/observation_space/),
while the [action space](http://miniwob.farama.org/content/action_space/) can be configured during environment construction.

# Citation

To cite this project please use:

```bibtex
@inproceedings{liu2018reinforcement,
 author = {Evan Zheran Liu and Kelvin Guu and Panupong Pasupat and Tianlin Shi and Percy Liang},
 title = {Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration},
 booktitle = {International Conference on Learning Representations ({ICLR})},
 url = {https://arxiv.org/abs/1802.08802},
 year = {2018},
}
```