cse151b-final-project/Gym-Wordle-main/README.md
2024-03-14 13:05:18 -07:00

79 lines
1.8 KiB
Markdown

# Gym-Wordle
An OpenAI gym compatible environment for training agents to play Wordle.
<p align='center'>
<img src="https://user-images.githubusercontent.com/8514041/152437216-d78e85f6-8049-4cb9-ae61-3c015a8a0e4f.gif"><br/>
<em>User-input demo of the environment</em>
</p>
## Installation
My goal is for a minimalist package that lets you install quickly and get on
with your research. Installation is just a simple call to `pip`:
```
$ pip install gym_wordle
```
### Requirements
In keeping with my desire to have a minimalist package, there are only three
major requirements:
* `numpy`
* `gym`
* `sty`, a lovely little package for stylizing text in terminals
## Usage
The basic flow for training agents with the `Wordle-v0` environment is the same
as with gym environments generally:
```Python
import gym
import gym_wordle
eng = gym.make("Wordle-v0")
done = False
while not done:
action = ... # RL magic
state, reward, done, info = env.step(action)
```
If you're like millions of other people, you're a Wordle-obsessive in your own
right. I have good news for you! The `Wordle-v0` environment currently has an
implemented `render` method, which allows you to see a human-friendly version
of the game. And it isn't so hard to set up the environment to play for
yourself. Here's an example script:
```Python
from gym_wordle.utils import play
play()
```
## Documentation
Coming soon!
## Examples
Coming soon!
## Citing
If you decide to use this project in your work, please consider a citation!
```bibtex
@misc{gym_wordle,
author = {Kraemer, David},
title = {An Environment for Reinforcement Learning with Wordle},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/DavidNKraemer/Gym-Wordle}},
}
```