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