macaw's Introduction

Offline Meta-Reinforcement Learning with Advantage Weighting (MACAW)

MACAW code used for the experiments in the ICML 2021 paper.

Installing the environment

# Install Python 3.7.9 if necessary
$ pyenv install 3.7.9
$ pyenv shell 3.7.9

$ python --version
Python 3.7.9

$ python -m venv env
$ source env/bin/activate
$ pip install -r requirements.txt

Downloading the data

The offline data used for MACAW can be found here. Download it and use the default name (macaw_offline_data) for the folder where the four data directories are stored. gDrive might be useful here if downloading from the Google Drive GUI is not an option.

Running MACAW 🦜

Run offline meta-training with periodic online evaluations with any of the scripts in scripts/. e.g.

$ . scripts/macaw_dir.sh # MACAW training on Cheetah-Direction (Figure 1)
$ . scripts/macaw_vel.sh # MACAW training on Cheetah-Velocity (Figure 1)
$ . scripts/macaw_quality_ablation.sh # Data quality ablation (Figure 5-left)
...

Outputs (tensorboard logs) will be written to the log/ directory.

Reach out!

If you're having issues with the code or data, feel free to open an issue or send me an email.

Citation

If our code or research was useful for your own work, you can cite us with the following attribution:

@InProceedings{mitchell2021offline,
    title = {Offline Meta-Reinforcement Learning with Advantage Weighting},
    author = {Mitchell, Eric and Rafailov, Rafael and Peng, Xue Bin and Levine, Sergey and Finn, Chelsea},
    booktitle = {Proceedings of the 38th International Conference on Machine Learning},
    year = {2021}
}

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