Visual Adversarial Imitation Learning using Variational Models (VMAIL)
This is the official implementation of the NeurIPS 2021 paper.
Method
VMAIL simultaneously learns a variational dynamics model and trains an on-policy
adversarial imitation learning algorithm in the latent space using only model-based
rollouts. This allows for stable and sample efficient training, as well as zero-shot
imitation learning by transfering the learned dynamics model
Instructions
Get dependencies:
conda env create -f vmail.yml
conda activate vmail
cd robel_claw/robel
pip install -e .
To train agents for each environmnet download the expert data from the provided link and run:
python3 -u vmail.py --logdir .logdir --expert_datadir expert_datadir
The training will generate tensorabord plots and GIFs in the log folder:
tensorboard --logdir ./logdir
Citation
If you find this code useful, please reference in your paper:
@article{rafailov2021visual,
title={Visual Adversarial Imitation Learning using Variational Models},
author={Rafael Rafailov and Tianhe Yu and Aravind Rajeswaran and Chelsea Finn},
year={2021},
journal={Neural Information Processing Systems}
}
