freor / domain-robust-visual-il

Domain-Robust Visual Imitation Learning with Mutual Information Constraints code

Domain-Robust Visual Imitation Learning with Mutual Information Constraints

This repository contains research code for the ICLR 2021 paper Domain-Robust Visual Imitation Learning with Mutual Information Constraints.

Requirements

1. To replicate the experiments in this project, you need to install the Mujoco simulation software with a valid license. You can find instructions here.

2. The rest of the requirements can be installed with conda, by utilizing the provided environment file:

conda env create -f environment.yml
conda activate autonomous_imitation

Collect data/Training

We explain how to collect expert/prior data and perform observational imitation to replicate the paper's experiments in the notebook training_notebook.ipynb. This notebook can be accessed via executing jupyter notebook after activating the conda environment.

Plotting

To reproduce the plots, we provide two functions to process and display the results collected:

• plot.py - to obtain a plot of multiple algorithms for a single observational imitation problem.

• multi_plot.py - to compare plots for multiple observational imitation problems partially sharing the same axis.

e.g. to plot the results for 1-Linked Inverted Pendulum -> 1-Linked Colored Inverted Pendulum:

python plot.py experiments_data/InvertedPendulum_to_colored --min_score 5 --max_score 50 --logdir . --epochs 20 --cumulative --show_legend --yaxis 'Scaled cumulative reward'

For further details on their usage, please run:

python plot.py -h

python multi_plot.py -h

Citation

@inproceedings{cetin2021domainrobust,
               title={Domain-Robust Visual Imitation Learning with Mutual Information Constraints},
               author={Edoardo Cetin and Oya Celiktutan},
               booktitle={International Conference on Learning Representations},
               year={2021},
               url={https://openreview.net/forum?id=QubpWYfdNry}
}