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

Related tags

Deep Learning lexa-benchmark
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.

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Comments
  • Problem in distance function / success criterion for dmc walker

    Problem in distance function / success criterion for dmc walker

    I noticed a problem in the distance function when running experiments on the dmc_walker_walk task.

    from lexa_benchmark.envs.dmc import DmcEnv

env = DmcEnv('walker_walk')
final_state = [-0.56107332, 0.20019225, 0.6347682, 1.75441328, 0.00908487, 0.7944932, 0.74205822, -1.85826755, -0.79472572]
env._compute_reward(goal_idx=9, pose=final_state)

    The above code will output (-0.6347682, 1.0) which means that the final distance to the goal is 0.6347682 and that the episode is considered successful.

    However, the final_state corresponds to the following observation: image

    and the goal is: image

    So we clearly see that the distance function and/or success threshold should be defined differently to assess whether the goal was achieved or not.

    opened by LinaMezghani 1
Owner
Oleg Rybkin
Ph.D. student with Kostas Daniilidis. I work on making machines think about the future.
Oleg Rybkin
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