What is gym-ignition?ΒΆ
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 iDynTree and exposing
high-level functionalities (idyntree).
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.