What is gym-gz?

gym-gz 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 Gz sim 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-gz 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-gz also provides powerful dynamics algorithms compatible with both fixed-base and floating-based robots by exploiting iDynTree and exposing high-level functionalities (idyntree).

Gym-gz 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_gz_environments package.