Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

Overview

LEXA Benchmark

Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models, NeurIPS 2021).

Setup

Create the conda environment by running : conda env create -f environment.yml

Alternatively, you can update an existing conda environment by running : conda env update -f environment.yml

Modify the python path
export PYTHONPATH=

Export the following variables for rendering
export MUJOCO_RENDERER=egl; export MUJOCO_GL=egl

Please follow these instructions to install mujoco

Bibtex

If you find this code useful, please cite:

@misc{lexa2021,
    title={Discovering and Achieving Goals via World Models},
    author={Mendonca, Russell and Rybkin, Oleh and
    Daniilidis, Kostas and Hafner, Danijar and Pathak, Deepak},
    year={2021},
    Booktitle={NeurIPS}
}

Acknowledgements

This benchmark is built on top of the following environments: Adept, MetaWorld, and DeepMind Control Suite.

Owner
Oleg Rybkin
Ph.D. student with Kostas Daniilidis. I work on making machines think about the future.
Oleg Rybkin
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Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

LEXA Benchmark Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models

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