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⚠️ Warning⚠️ This project is no longer actively maintained, and development has stalled. For an in-depth description of the current status and actionable steps to revive development, please consult robotology-legacy#430.
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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.
- First, follow the installation instructions of ScenarIO.
pip install gym-ignition, preferably in a virtual environment.
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.
@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}
} 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.