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[NeurIPS 2022] EfficientImitate

This is the codebase of our paper ''Planning for Sample Efficient Imitation Learning'' at the NeurIPS 2022.

Preparation

Step 1: Preparing python packages. This project is dependent on the following packages ray, torch, dmc2gym, opencv-python, and kornia. We can use pip to install them.

Step 2: Compiling cpp source. After installing these dependencies, we need to compile the C++ source of the MCTS as follows.

cd mcts_tree_sample
bash make.sh

Step 3: Download the data. Finally, we need to download the demonstration data at GoogleDrive, and put them into the ./data folder.

Training

We put the launch scripts at the ./scripts folder. For example, you can launch the training of walker by

bash ./scripts/walker_state.sh

Note that the training is carried out on a server with 4 NVIDIA 3090 GPUs, 128 CPU Cores, and 512GB RAM.

Citation

If you find this work useful and would like to cite it in your research:

@inproceedings{efficientimitate,
  title={Planning for Sample Efficient Imitation Learning},
  author={Yin, Zhao-Heng and Ye, Weirui and Chen, Qifeng and Gao, Yang},
  booktitle={Neural Information Processing Systems},
  year={2022}
}

License

MIT License