metadata

title: Wordle Solver
emoji: 🔠
colorFrom: green
colorTo: yellow
sdk: streamlit
sdk_version: 1.29.0
pinned: false
app_file: streamlit_app.py

Wordle Solver with RL

A model that solves Wordle using reinforcement learning

The solution and code is a merge of Andrew Ho reinforcement learning solution and Morvan Zhou a3c Pytorch implementation

Instalation

Just make sure you have installed:

python >= 3.8
pip >= 22.3.1 And then, on the project root, execute:

pip install -r requirements.txt

CLI Running and execution modes

To run the program, on the project root execute:

python main.py [ENV][MODE] [PARAMETERS]

[ENV] --> is the enviroment or size of the game loaded. It can be:
- WordleEnv100OneAction-v0 --> 100 elegible words but only one goal word
- WordleEnv100TwoAction-v0 --> 100 elegible words but only two goal words
- WordleEnv100fiftyAction-v0 -> 100 elegible words but fifty goal words
- WordleEnv100FullAction-v0 --> 100 elegible words and all words can be the goal word
- WordleEnv1000FullAction-v0 --> 1000 elegible words and all words can be the goal word
- WordleEnvFull-v0 --> All possible elegible words (2300) and all words can be the goal word
[MODE] --> is the execution mode
[PARAMETERS] --> are the arguments needed to run a certain mode.

There are three possible execution modes:

train --> Train a model (or models) to learn to play wordle with different game sizes and hyperparameters
eval --> Evaluate the trained model(s) saved for a particular game size
play --> Have a model suggest what is the next word for a wordle game given the words already tried and the game outputs

Below each of the execution modes are explained more detailed.

Training

For training a model, run:

python main.py [ENV] train -g [GAMES] --gamma [GAMMA] --seed [SEED] --save --min_reward [MIN_REWARD] --every_n_save [EVERY_N] --model_name [MODEL_NAME]

Parameters:

[ENV] --> the enviroment or game size for which the model will be trained, they are listed in the above section
-g [GAMES] --> number of training games played
--gamma [GAMMA] --> optional, default 0, the discount factor for the reward obtained by playing each step of the game
--seed [SEED] --> optional, default 100, seed used for the generation of random numbers
--save --> optional, default False, if present, instances of the trained model are saved while training
--min_reward [MIN_REWARD] --> optional, default 9.9, only works if --save is present, the min global reward the model has to reach to be saved
--every_n_save [EVERY_N] --> optional, default 100, only works if --save is present, inicates how often the model is saved (whether the model is saved or not depends on --min_reward)
--model_name [MODEL_NAME] --> optional, the name of the saved model if want to train from a pretrained model.

Evaluation

For evaluation a model of a particular enviroment, run:

python main.py [ENV] eval

[ENV] --> the environment on which the models will be evaluated, only models trained for that specific environment will be evaluated.

Play

For word suggestion, run:

python main.py enviroment play --words [WORDS] --states [STATES] --model_name [MODEL_NAME]

[ENV] --> the environment or game size from which a word is suggested
--words [WORDS] --> List of words played in the wordle game which is being played
--states [STATES] --> List of states returned by the result of playing each of the words, the state must be represented following this rules:
- a 0 if the letter wasn't in the word
- a 1 if the letter was in the word but not in the correct position
- a 2 if the letter was in the word and in the correct position
--model_name [MODEL_NAME] --> Name of the pretrained model file which will play the game

Play Wordle with the help of an AI using Streamlit

To run a Streamlit app to play wordle with the help of an AI assistant you need to:

Have a pretrained model that knows how to play wordle:
1. You need to create a .env file in the project root with RS_WORDLE_MODEL_NAME property.
2. Download the pretrained model and put it under checkpoints dir, to do that you have two options:
  - Option 1: Download it manually from our Huggingface repo.
  - Option 2: Let it download automatically by setting an HF_MODEL_REPO_NAME property with value rootstrap-org/wordle-solver in the .env file. You have to be authenticated in huggingface with huggingface_cli to do that.
3. Set the RS_WORDLE_MODEL_NAME property in .env with the model filename.
Run the app by executing streamlit run streamlit.py

Make the model play RS-Wordle alone!

It is possible to make the model play the Rootstrap version of Wordle. To do that, you need to:

Have a Rootstrap mail account
Get access to the Firebase database of the RS-Wordle instance and a private key obtained from the Service accounts Project Setting. This is for authentication and has to be stored as a JSON file.
Use a pre-trained model that will play the game, and put it under checkpoints dir, to do that you have two options:
- Option 1: Download it manually from our Huggingface repo.
- Option 2: Let it download automatically by setting an HF_MODEL_REPO_NAME property with value rootstrap-org/wordle-solver in the .env file. You have to be authenticated in Huggingface with huggingface_cli to do that.

Then, you need to add to the .env file the following properties:

RS_FIREBASE_CREDENTIALS_PATH=path/to/private_key.json
RS_WORDLE_USER=rootstrap_mail_user
RS_WORDLE_URL=rs_wordle_instance_url
RS_WORDLE_PASSWORD=rootstrap_mail_password

Using Selenium, the program simulates user interaction with the browser and makes the model play the game, retrieving the result of each attempt from the Firebase database. To start the fun, navigate to the project root in your terminal and execute:

python -m rs_wordle_player.rs_wordle_player

Branches

There are three different working branches on the project, each one with a different representation of the state of the game

main --> state represented as one hot encoding of letters and the state of the letters (if the letter was or not in the word) in every position of the words guessed
cosin-state --> state represented as the letters used in every guess and the state of letters in each guess. Letter are represented as a pair of cos and sin functions applied to an numerical representation of the letters
simpler-state --> state represented as the letters used in the current guess and the state of letters in the guess. Letter are represented by converting their integer representation to binary