Gym environment for the game Wordle. A simple action, state, and observation is included, with a random agent. Note that the spaces are very simplistic, and only included for a working example of the env.
- Python >=3.8 required
- Create python virtual environment and install the
requirements.txtfile:
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
- From the parent folder, install SpellBond as a package:
pip install -e .
To execute inference on full vocab, run the following command from scripts folder:
cd scripts
python run.py --task infer --test-file <path to testfile>
The test-file has a default value of "words.txt".
Optionally, to execute inference on partial vocab size, run the following:
NOTE: Before executing, update the test-file to contain words only from this partial vocab list
python run.py --vocab-size {vocab_size} --task infer --test-file <path to testfile>
vocab_size can either be None (default, full vocab) or 1000.
Wordle_RLclass represents the ACLA RL algorithm (adapted CACLA to discrete state/action spaces)SARSAclass represents the actor-critic RL algorithm which can be trained either on-policy or off-policy