This repository contains code for the compositional learning framework presented in the paper Robust Subtask Learning for Compositional Generalization by Kishor Jothimurugan, Steve Hsu, Osbert Bastani and Rajeev Alur, published in ICML 2023.
Python version is 3.7.10. Install dependencies in requirements.txt, preferably in a virtual environment:
pip install -r requirements.txt
To run the experiments presented in the papar, move to the directory corresponding to the environemnt. For rooms environment,
cd test/rooms
For F110 environment,
cd test/f110_turn
After changing the working directory appropriately, run the command corresponding to the algorithm to use. To run ROSAC:
python masac.py -d {save_directory} -n {run_number} -v {gpu_number} -g -z
For the ablation in which subtasks are picked randomly during training, use the above command without the -z option. To run AROSAC,
the asynchronous version of the algorithm:
python cegrl.py -d {save_directory} -n {run_number} -v {gpu_number} -g -z -c
In the above command, omit option -z for the DAGGER baseline and omit both -z and -c for the NAIVE baseline. To run the MADDPG baseline,
python maddpg.py -d {save_directory} -n {run_number}
To run the PAIRED baseline,
python paired.py d {save_directory} -n {run_number} -v {gpu_number} -g