- Create the conda environment:
conda create -n cb_vin python=3.7 - Make sure that git-LFS is installed before cloning the repo.
- Clone the repo.
- Install the requirements:
pip install -r requirements.txt - Unzip the preprocessed data:
unzip preprocessed.zip. This will create a new directorypreprocessed/including two subdirectories: (1)examples/includes a pickle file for each example in the dataset; the filenames are prefixed with which split the example is from. (2)games/includes a pickle file for each original CerealBar game, which includes the game ID and all the static environment information for that game. - Make a directory to store experiments:
mkdir experiments/
You will need to install pytorch separately according to your machine's requirements. We tested using torch 1.6.0 and 1.2.0. See this page for details.
config/defines configuration files, including for running the program, model parameters, etc. Configurations are dataclasses and stored as JSON files on disk in thejson_configs/file.data/defines classes and functions for loading and storing data, including instructions and game information.environment/defines information about the CerealBar environment, including props, positions, cards, etc.evaluation/is used to evaluate models; defines metrics, evaluation functions, and evaluation loops.experiments/contains subdirectories for each experiment ran.inference/is used to run inference on a model.json_configs/includes configurations for running the program.learning/is used to train a model.protobuf/is used to store the protobuf format for communication with the standalone or the web server.model/defines the model architectures.preprocessed/contains preprocessed data (instruction examples and games).simulation/contains code for simulating the CerealBar game, either through the Unity standalone or a python copy.util/contains various utilities.web_agent/contains code necessary for running the model in live games in browser.