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Masked Autoencoding for Scalable and Generalizable Decision Making

This is the official implementation for the paper Masked Autoencoding for Scalable and Generalizable Decision Making .

@inproceedings{liu2022masked,
    title={Masked Autoencoding for Scalable and Generalizable Decision Making},
    author={Liu, Fangchen and Liu, Hao and Grover, Aditya and Abbeel, Pieter},
    booktitle={Advances in Neural Information Processing Systems},
    year={2022}
}

Installation

Install the following libraries:

sudo apt update
sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3 unzip

Install dependencies:

conda env create -f conda_env.yml
conda activate maskdp

Dataset

Download precollected dataset

We provide the datasets used in the paper on HuggingFace. You can download the dataset with the command:

git clone [email protected]:datasets/fangchenliu/maskdp_data

The dataset is organized in the following format:

├── maskdp_train
│   ├── cheetah
│   │   ├── expert # near-expert rollouts from TD3 policy
|   |   |   ├── cheetah_run
|   |   |   |   ├── 0.npy
|   |   |   |   ├── 1.npy
|   |   |   |   ├── ...
|   |   |   ├── cheetah_run_backwards
│   │   ├── sup # supervised data, full experience replay with extrinsic reward
|   |   |   ├── cheetah_run
|   |   |   ├── cheetah_run_backwards
│   │   ├── semi # semi-supervised data, full experience replay with extrinsic + intrinsic reward
|   |   |   ├── cheetah_run
|   |   |   ├── cheetah_run_backwards
│   │   ├── unsup # unsupervised data, full experience replay with intrinsic reward
|   |   |   ├── 0.npy
|   |   |   ├── 1.npy
|   |   |   ├── ...
│   ├── walker
...
│   ├── quadruped
...
├── maskdp_eval
│   ├── expert
│   │   ├── cheetah_run
│   │   ├── cheetah_run_backwards
│   │   ├── ...
│   │   ├── walker_stand
│   │   ├── quadruped_walk
│   │   ├── ...
│   ├── unsup
│   │   ├── cheetah
│   │   ├── walker
│   │   ├── quadruped

Collect your own dataset

If you want to customize your own dataset on different environments, please follow the instructions in the data_collection branch.

Example Scripts

We provide example scripts in train and eval folder to train or evaluate the model. Please modify the path to your local dataset.

Acknowledgement

  • This project is inspired by ExoRL. We use the same environment and data collection pipeline.

  • The transformer implementation is adapted from minGPT and original MAE.

Contact

If you have any questions, please open an issue or contact [email protected].