gym-ignition

Description

gym-ignition is a framework to create reproducible robotics environments for reinforcement learning research.

It is based on the 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 and Runtime abstractions that help you focusing on the development of the decision-making logic rather than engineering. It includes 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 and exposing high-level functionalities.

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 package.

Visit the website for more information about the project.

Installation

First, follow the installation instructions of ScenarIO.
pip install gym-ignition, preferably in a virtual environment.

Contributing

You can visit our community forum hosted in GitHub 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 section! You can advertise there your environments created with gym-ignition.

Pull requests are welcome.

For major changes, please open a discussion first to propose what you would like to change.

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}
}

License

LGPL v2.1 or any later version.

Disclaimer: Gym-ignition is an independent project and is not related by any means to OpenAI and Open Robotics.

Metadata

Release files for gym-ignition 1.2.3.dev163

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for gym-ignition 1.2.3.dev163
File	Size	Uploaded
gym_ignition-1.2.3.dev163.tar.gz	252.1 kB	Oct 3, 2021	Details

Built distribution (wheel)

Table of built distributions (wheels) for gym-ignition 1.2.3.dev163
File	Interpreter	ABI	Platform	Reset
gym_ignition-1.2.3.dev163-py3-none-any.whl 62.4 kB Oct 3, 2021	Python 3	none	any	Details

Total release size: 314.5 kB

Release files / gym_ignition-1.2.3.dev163.tar.gz

Download URL	gym_ignition-1.2.3.dev163.tar.gz
Size	252.1 kB
Tags	Source
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BLAKE2b-256 checksum How to use checksums	`f313398f5ba25ab122024203f8ca3946094f36d7d7a83e74b2df2419b787ad90`
Upload date	Oct 3, 2021
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7`

Release files / gym_ignition-1.2.3.dev163-py3-none-any.whl

Download URL	gym_ignition-1.2.3.dev163-py3-none-any.whl
Size	62.4 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`7e7d224966327ca08dc15b0f7d5970176c49e8d80f75f5197742c8ab41010d79`
BLAKE2b-256 checksum How to use checksums	`ecd5d549ad824345fe53aef59eb979fe3441f030dc2e976fe67be60a65cb987d`
Upload date	Oct 3, 2021
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7`