Envrionment for pyboy games for Reinforcement Learning training
git clone the repository into your desired directory on your local machine
Run pip3 install -r requirements.txt in the root directory of the package
To make the module globally accessible in your working environment run pip3 install --editable . in the project root
This package provides the baseline code for the pyboy environments - you run these envrionments through gymnasium_envrionment.
train.py takes in hyperparameters that allow you to customise the training run enviromment – OpenAI or DMCS Environment - or RL algorithm. Use python3 train.py -h for help on what parameters are available for customisation.
An example is found below for running on the pyboy environments with TD3 through console
python3 train.py run --gym pyboy --task mario TD3
Environment running Gameboy games utilising the pyboy wrapper: https://github.com/UoA-CARES/pyboy_environment
These games are run through the generalised gymnaisum environment: https://github.com/UoA-CARES/gymnasium_envrionments
python3 train.py run --gym pyboy --domain mario --task run SACAE
python3 train.py run --gym pyboy --domain pokemon --task catch SACAE
