Metadata-Version: 2.1
Name: gym-ignition
Version: 1.2.3.dev59
Summary: Gym-Ignition: A toolkit for developing OpenAI Gym environments simulated with Ignition Gazebo.
Home-page: https://github.com/robotology/gym-ignition
Author: Diego Ferigo
Author-email: diego.ferigo@iit.it
License: LGPL
Project-URL: Bug Tracker, https://github.com/robotology/gym-ignition/issues
Project-URL: Documentation, https://robotology.github.io/gym-ignition
Project-URL: Source Code, https://github.com/robotology/gym-ignition
Description: <p align="center">
        <h1 align="center">gym-ignition</h1>
        </p>
        
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        ||||
        |:---:|:---:|:---:|
        | ![][pendulum] | ![][panda] | ![][icub] |
        
        [icub]: https://user-images.githubusercontent.com/469199/99262746-9e021a80-281e-11eb-9df1-d70134b0801a.png
        [panda]: https://user-images.githubusercontent.com/469199/99263111-0cdf7380-281f-11eb-9cfe-338b2aae0503.png
        [pendulum]: https://user-images.githubusercontent.com/469199/99262383-321fb200-281e-11eb-89cc-cc31f590daa3.png
        
        ## Description
        
        **gym-ignition** is a framework to create **reproducible robotics environments** for reinforcement learning research.
        
        It is based on the [ScenarIO](scenario/) project which provides the low-level APIs to interface with the Ignition Gazebo simulator.
        By default, RL environments share a lot of boilerplate code, e.g. for initializing the simulator or structuring the classes
        to expose the `gym.Env` interface.
        Gym-ignition provides the [`Task`](python/gym_ignition/base/task.py) and [`Runtime`](python/gym_ignition/base/runtime.py)
        abstractions that help you focusing on the development of the decision-making logic rather than engineering.
        It includes [randomizers](python/gym_ignition/randomizers) to simplify the implementation of domain randomization
        of models, physics, and tasks.
        Gym-ignition also provides powerful dynamics algorithms compatible with both fixed-base and floating-based robots by
        exploiting [robotology/idyntree](https://github.com/robotology/idyntree/) and exposing
        [high-level functionalities](python/gym_ignition/rbd/idyntree).
        
        Gym-ignition does not provide out-of-the-box environments ready to be used.
        Rather, its aim is simplifying and streamlining their development.
        Nonetheless, for illustrative purpose, it includes canonical examples in the
        [`gym_ignition_environments`](python/gym_ignition_environments) package.
        
        Visit the [website][website] for more information about the project.
        
        [website]: https://robotology.github.io/gym-ignition
        
        ## Installation
        
        1. First, follow the installation instructions of [ScenarIO](scenario/).
        2. `pip install gym-ignition`, preferably in a [virtual environment](https://docs.python.org/3.8/tutorial/venv.html).
        
        ## Contributing
        
        You can visit our community forum hosted in [GitHub Discussions](https://github.com/robotology/gym-ignition/discussions).
        Even without coding skills, replying user's questions is a great way of contributing.
        If you use gym-ignition in your application and want to show it off, visit the
        [Show and tell](https://github.com/robotology/gym-ignition/discussions/categories/show-and-tell) section!
        You can advertise there your environments created with gym-ignition.
        
        Pull requests are welcome.
        
        For major changes, please open a [discussion](https://github.com/robotology/gym-ignition/discussions)
        first to propose what you would like to change.
        
        ## Citation
        
        ```bibtex
        @INPROCEEDINGS{ferigo2020gymignition,
            title={Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning},
            author={D. {Ferigo} and S. {Traversaro} and G. {Metta} and D. {Pucci}},
            booktitle={2020 IEEE/SICE International Symposium on System Integration (SII)},
            year={2020},
            pages={885-890},
            doi={10.1109/SII46433.2020.9025951}
        } 
        ```
        
        ## License
        
        [LGPL v2.1](https://choosealicense.com/licenses/lgpl-2.1/) or any later version.
        
        ---
        
        **Disclaimer:** Gym-ignition is an independent project and is not related by any means to OpenAI and Open Robotics.
        
Keywords: openai,gym,reinforcement learning,rl,environment,gazebo,robotics,ignition,humanoid,panda,icub,urdf,sdf
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Games/Entertainment :: Simulation
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development
Classifier: Framework :: Robot Framework
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: License :: OSI Approved :: GNU Lesser General Public License v2 or later (LGPLv2+)
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: website
Provides-Extra: test
