104 lines
3.3 KiB
ReStructuredText
104 lines
3.3 KiB
ReStructuredText
Fancy Gym
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=========
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Built upon the foundation of
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`Gymnasium <https://gymnasium.farama.org/>`__ (a maintained fork of
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OpenAI’s renowned Gym library) ``fancy_gym`` offers a comprehensive
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collection of reinforcement learning environments.
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Key Features
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------------
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- **New Challenging Environments**: ``fancy_gym`` includes several new
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environments (Panda Box Pushing, Table Tennis, etc.) that present a
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higher degree of difficulty, pushing the boundaries of reinforcement
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learning research.
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- **Support for Movement Primitives**: ``fancy_gym`` supports a range
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of movement primitives (MPs), including Dynamic Movement Primitives
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(DMPs), Probabilistic Movement Primitives (ProMP), and Probabilistic
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Dynamic Movement Primitives (ProDMP).
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- **Upgrade to Movement Primitives**: With our framework, it’s
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straightforward to transform standard Gymnasium environments into
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environments that support movement primitives.
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- **Benchmark Suite Compatibility**: ``fancy_gym`` makes it easy to
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access renowned benchmark suites such as `DeepMind
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Control <https://deepmind.com/research/publications/2020/dm-control-Software-and-Tasks-for-Continuous-Control>`__
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and `Metaworld <https://meta-world.github.io/>`__, whether you want
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to use them in the regular step-based setting or using MPs.
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- **Contribute Your Own Environments**: If you’re inspired to create
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custom gym environments, both step-based and with movement
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primitives, this
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`guide <https://gymnasium.farama.org/tutorials/gymnasium_basics/environment_creation/>`__
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will assist you. We encourage and highly appreciate submissions via
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PRs to integrate these environments into ``fancy_gym``.
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.. toctree::
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:maxdepth: 3
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:caption: User Guide
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guide/installation
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guide/episodic_rl
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guide/basic_usage
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guide/upgrading_envs
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.. toctree::
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:maxdepth: 3
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:caption: Environments
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envs/fancy/index
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envs/dmc
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envs/meta
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envs/open_ai
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.. toctree::
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:maxdepth: 3
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:caption: Examples
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examples/general
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examples/dmc
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examples/metaworld
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examples/open_ai
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examples/movement_primitives
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examples/mp_params_tuning
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examples/pd_control_gain_tuning
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examples/replanning_envs
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.. toctree::
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:maxdepth: 3
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:caption: API
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api
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Citing the Project
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------------------
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To cite this repository in publications:
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.. code:: bibtex
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@software{fancy_gym,
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title = {Fancy Gym},
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author = {Otto, Fabian and Celik, Onur and Roth, Dominik and Zhou, Hongyi},
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abstract = {Fancy Gym: Unifying interface for various RL benchmarks with support for Black Box approaches.},
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url = {https://github.com/ALRhub/fancy_gym},
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organization = {Autonomous Learning Robots Lab (ALR) at KIT},
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}
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Icon Attribution
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----------------
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The icon is based on the
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`Gymnasium <https://github.com/Farama-Foundation/Gymnasium>`__ icon as
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can be found
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`here <https://gymnasium.farama.org/_static/img/gymnasium_black.svg>`__.
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=================
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.. raw:: html
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<div style="text-align: center;">
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<a href="https://alr.anthropomatik.kit.edu/"><img src="_static/imgs/alr.svg" style="margin: 5%; width: 20%;"></a>
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<a href="https://www.kit.edu/"><img src="_static/imgs/kit.svg" style="margin: 5%; width: 20%;"></a>
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<a href="https://uni-tuebingen.de/"><img src="_static/imgs/uni_tuebingen.svg" style="margin: 5%; width: 20%;"></a>
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</div>
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