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README.md

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Installation

  1. Create the conda environment: conda create -n cb_vin python=3.7
  2. Make sure that git-LFS is installed before cloning the repo.
  3. Clone the repo.
  4. Install the requirements: pip install -r requirements.txt
  5. Unzip the preprocessed data: unzip preprocessed.zip. This will create a new directory preprocessed/ 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.
  6. 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.

Subdirectories

  • config/ defines configuration files, including for running the program, model parameters, etc. Configurations are dataclasses and stored as JSON files on disk in the json_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.