PLEX-Metaworld is a version of a flavor of Meta-World v2 that was used in the experiments of the PLEX architecture paper and is installed as a dependency of PLEX. PLEX-Metaworld was created by forking the original Meta-World at commit a0009ed. PLEX-Metaworld's main difference from the original Meta-World is a wrapper for Meta-World tasks that returns pixel observations, proprioceptive states and, optionally, depth maps as observations.
For more background information on Meta-World, please refer to the Meta-World website and the accompanying conference publication.
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Meta-World is based on MuJoCo, which has a proprietary dependency we can't set up for you. Please follow the instructions in the mujoco-py package for help. Once you're ready to install everything, run:
pip install metaworld@git+https://github.com/microsoft/PLEX-MetaWorld
Alternatively, you can clone the repository and install an editable version locally:
git clone https://github.com/microsoft/PLEX-MetaWorld.git
cd metaworld
pip install -e .
For the purposes of reproducibility, it may be important to you to seed your benchmark instance. You can do so in the following way:
import metaworld
SEED = 0 # some seed number here
benchmark = metaworld.BENCHMARK(seed=SEED)See the metaworld.mw_gym_make(.) method. For an example of how to use it, see the data/training_data_gen.py script.
See the data/training_data_gen.py script.
The original Meta-World was a work by Tianhe Yu (Stanford University), Deirdre Quillen (UC Berkeley), Zhanpeng He (Columbia University), Ryan Julian (University of Southern California), Karol Hausman (Google AI), Chelsea Finn (Stanford University) and Sergey Levine (UC Berkeley). Currently, the canonical Meta-World version is maintained by Farama Foundation.
In turn, the code for Meta-World was originally based on multiworld, which is developed by Vitchyr H. Pong, Murtaza Dalal, Ashvin Nair, Shikhar Bahl, Steven Lin, Soroush Nasiriany, Kristian Hartikainen and Coline Devin. The Meta-World authors are grateful for their efforts on providing such a great framework as a foundation of our work. We also would like to thank Russell Mendonca for his work on reward functions for some of the environments.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
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