Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning

by   Diego Ferigo, et al.

In this paper we present 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. The new Ignition Gazebo simulator mainly provides three 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, and they can be switched during runtime; 3) the new distributed simulation capability permits 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 it simplifies its configuration and usage. We provide a Python package that permits 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, and 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 from the specific runtime. This allows their execution also in a real-time setting on actual robotic platforms.


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