This is the code for the GridCoder algorithm mentioned in my ARC Prize 2024 paper submission:

• Towards Efficient Neurally-Guided Program Induction for ARC-AGI

Setup

• First, clone ARC_gym
• From the ARC_gym local repo folder, pip -e install .
• Then, clone ARC-AGI into the GridCoder2024 root folder, because you will need ARC-AGI grid examples for training/validation data generation.
• Rename the ARC-AGI folder to ARC
• You can get pre-trained weights from my Kaggle model: https://www.kaggle.com/models/simonouellette/gridcoder-2024/ -- download the model_full.pth file under Kaggle_code/

Usage

• Training the model: train_full.py (model/LVM.py is the model architecture itself).
• Testing the solution: test_gridcoder.py (see Kaggle notebook for details on using it in the context of a Kaggle competition).
• Generating training data: generate_training_data_full.py
• The training data generation code is under the datasets/ folder.
• The main search algorithm itself is search/p_star_superposition.py (Note: I originally called the algorithm P* as a play on A*, but in the paper I refer to this as GridCoder).
• P_star.py is "GridCoder cond." in the paper.
• P_star_muzero.py is the MCTS-based variant that was evaluated in the paper.