Metadata-Version: 2.1
Name: gym-ignition
Version: 1.2.2.dev20
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
Description: <p align="center">
        <h1 align="center">gym-ignition</h1>
        </p>
        
        <p align="center">
        <b><a href="https://github.com/robotology/gym-ignition#description">Description</a></b>
        •
        <b><a href="https://github.com/robotology/gym-ignition#setup">Setup</a></b>
        •
        <b><a href="https://github.com/robotology/gym-ignition#citation">Citation</a></b>
        •
        <b><a href="https://robotology.github.io/gym-ignition/master/index.html">Website</a></b>
        </p>
        
        <div align="center">
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                    <td align="left">General</td>
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                        <a href="https://isocpp.org">
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                        <a href="https://github.com/robotology/gym-ignition/actions">
                        <img src="https://github.com/robotology/gym-ignition/workflows/CI/CD/badge.svg" alt="CICD" />
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                <tr>
                    <td align="left"><code>gym-ignition</code></td>
                    <td align="center">
                        <a href="https://pypi.org/project/gym-ignition/">
                        <img src="https://img.shields.io/pypi/v/gym-ignition.svg" />
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                        <a href="https://pypi.org/project/gym-ignition/">
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                        <a href="https://pypi.org/project/gym-ignition/">
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        ## Description
        
        **gym-ignition** is a framework to create **reproducible robotics environments** for reinforcement learning research.
        
        The project consists of the following components:
        
        - [**`ScenarI/O`**](cpp/scenario/core): 
          *Scene Interfaces for Robot Input / Output* is a C++ abstraction layer to interact with simulated and real robots.
        - [**`Gazebo ScenarI/O`**](cpp/scenario/gazebo): 
          Implementation of the ScenarI/O interfaces to interact with the [Ignition Gazebo](https://ignitionrobotics.org) simulator. 
          We provide Python bindings with functionalities comparable to popular alternatives like 
          [pybullet](https://github.com/bulletphysics/bullet3) and [mujoco-py](https://github.com/openai/mujoco-py).
        - [**`gym_ignition`**](python/gym_ignition): 
          A Python package with the tooling to create OpenAI Gym environments for robot learning. 
          It provides abstractions like `Task` and `Runtime` to help developing environments that can be executed transparently 
          on all the ScenarI/O implementations (different simulators, real robots, ...).
          The package also contains resources for inverse kinematics and multi-body dynamics supporting floating-based robots
          based on the [iDynTree](https://github.com/robotology/idyntree) library.
        - [**`gym_ignition_environments`**](python/gym_ignition_environments):
          Demo environments created with `gym_ignition` and [`gym-ignition-models`](https://github.com/dic-iit/gym-ignition-models) 
          that show the recommended structure.
          
        This project provides the complete implementation of ScenarI/O for the Ignition Gazebo simulator.
        We expose all the physics engines supported by Ignition Gazebo.
        Currently, the default and only physics engine is [DART](https://github.com/dartsim/dart).
        
        We are currently working on backends based on robotic middleware to transparently execute the environments developed 
        with `gym_ignition` on real robots.
        
        If you're interested to know the reasons why we started developing gym-ignition and why we selected Ignition Gazebo for
        our simulations, visit the _Motivations_ section of the [website](https://robotology.github.io/gym-ignition). 
        
        ## Setup
        
        1. Install the Ignition suite following the [official instructions](https://ignitionrobotics.org/docs/edifice).
        1. Execute `pip install gym-ignition`, preferably in a virtual environment.
        
        **Note**: `gym-ignition` currently only supports the latest version of the ignition suite. For more information on supported versions please refer to the [Support Policy](https://robotology.github.io/gym-ignition/master/installation/support_policy.html).
        
        
        Then, for some simple examples, visit the _Getting Started_ section of the [website](https://robotology.github.io/gym-ignition).
        
        You can decide to install only the C++ resources if you are not interested in using Python.
        We also offer a constantly updated pre-release channel with the last development updates.
        You can find all the details about the different types of installations we support in the [website](https://robotology.github.io/gym-ignition).
        
        ||||
        |:---:|:---:|:---:|
        | ![](https://user-images.githubusercontent.com/469199/99262383-321fb200-281e-11eb-89cc-cc31f590daa3.png) | ![](https://user-images.githubusercontent.com/469199/99263111-0cdf7380-281f-11eb-9cfe-338b2aae0503.png) | ![](https://user-images.githubusercontent.com/469199/99262746-9e021a80-281e-11eb-9df1-d70134b0801a.png) |
        
        ## Citation
        
        ```
        @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}
        } 
        ```
        
        ---
        
        **Disclaimer:** Gym-Ignition is an independent project and is not related by any means to OpenAI and Open Robotics.
        
Keywords: openai gym reinforcement learning environment gazebo robotics ignition
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: Framework :: Robot Framework
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: C++
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: test
Provides-Extra: website
