Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning

• Published in: arXiv.org
• Main Authors: Ferigo, Diego, Traversaro, Silvio, Metta, Giorgio, Pucci, Daniele
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 02.12.2019
• Subjects: Algorithms; Computer architecture; Computer Science - Distributed, Parallel, and Cluster Computing; Computer Science - Learning; Computer Science - Robotics; Computer Science - Systems and Control; Computer simulation; Graphical user interface; Ignition; Machine learning; Parallel processing; Robotics; Software; Time setting
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1911.01715

This paper presents Gym-Ignition, a new framework to create reproducible robotic environments for reinforcement learning research. It interfaces with the new generation of Gazebo, part of the Ignition Robotics suite, which provides three main improvements for reinforcement learning applications compared to the alternatives: 1) the modular architecture enables using the simulator as a C++ library, simplifying the interconnection with external software; 2) multiple physics and rendering engines are supported as plugins, simplifying their selection during the execution; 3) the new distributed simulation capability allows simulating complex scenarios while sharing the load on multiple workers and machines. The core of Gym-Ignition is a component that contains the Ignition Gazebo simulator and exposes a simple interface for its configuration and execution. We provide a Python package that allows developers to create robotic environments simulated in Ignition Gazebo. Environments expose the common OpenAI Gym interface, making them compatible out-of-the-box with third-party frameworks containing reinforcement learning algorithms. Simulations can be executed in both headless and GUI mode, the physics engine can run in accelerated mode, and instances can be parallelized. Furthermore, the Gym-Ignition software architecture provides abstraction of the Robot and the Task, making environments agnostic on the specific runtime. This abstraction allows their execution also in a real-time setting on actual robotic platforms, even if driven by different middlewares.