|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Rex Gym||759||2 years ago||17||October 08, 2020||9||apache-2.0||Python|
|OpenAI Gym environments for an open-source quadruped robot (SpotMicro)|
|Spot_mini_mini||696||6 months ago||11||mit||C++|
|Dynamics and Domain Randomized Gait Modulation with Bezier Curves for Sim-to-Real Legged Locomotion.|
|Gymfc||242||a year ago||8||mit||Python|
|A universal flight control tuning framework|
|Stable Baselines||221||7 months ago||1||mit||Python|
|Mirror of Stable-Baselines: a fork of OpenAI Baselines, implementations of reinforcement learning algorithms|
|Gym Electric Motor||216||a month ago||12||June 15, 2022||11||mit||Python|
|Gym Electric Motor (GEM): An OpenAI Gym Environment for Electric Motors|
|Skrl||192||7 days ago||6||January 26, 2023||2||mit||Python|
|Modular and flexible reinforcement learning library with support for Isaac Gym and Omniverse Isaac Gym environments|
|Gym Fx||187||3 months ago||1||Python|
|Forex trading simulator environment for OpenAI Gym, observations contain the order status, performance and timeseries loaded from a CSV file containing rates and indicators. Work In Progress|
|Neuroflight||182||2 years ago||n,ull||gpl-3.0||C|
|Next Generation Flight Controller Firmware|
|Jiminy||132||3||15 days ago||138||June 17, 2022||40||mit||Jupyter Notebook|
|Jiminy: a fast and portable Python/C++ simulator of poly-articulated robots with OpenAI Gym interface for reinforcement learning|
|Cartpole||111||2 years ago||2||mit||Python|
|OpenAI's cartpole env solver.|
Jiminy is a cross-platform open-source simulator for poly-articulated systems. It was built with two ideas in mind:
Jiminy is built around Pinocchio, an open-source fast and efficient kinematics and dynamics library. Jiminy thus uses minimal coordinates and Lagrangian dynamics to simulate an articulated system: this makes Jiminy as close as numerically possible to an analytical solution, without the risk of joint violation.
Beside a strong focus on performance to answer machine learning's need for running computationally demanding distributed simulations, Jiminy offers convenience tools for learning via a dedicated module Gym-Jiminy. It is fully compliant with
gym standard API and provides a highly customizable wrapper to interface any robotics system with state-of-the-art learning frameworks.
pipis all that is needed to get you started ! Support Linux, Mac and Windows platforms.
A more complete list of features is available on the wiki.
The documentation is available on Github.io, or locally in
docs/html/index.html if built from source.
Gym Jiminy is an interface between Jiminy simulator and reinforcement learning frameworks. It is fully compliant with now standard Open AI Gym API. Additionally, it offers a generic and easily configurable learning environment for learning locomotion tasks, with minimal intervention from the user, who usually only needs to provide the robot's URDF file. Furthermore, Gym Jiminy enables easy modification of many aspects of the simulation to provide richer exploration and ensure robust learning. This ranges from external perturbation forces to sensor noise and bias, including randomization of masses and inertias, ground friction model or even gravity itself. Note that learning can easily be done on any high-level dynamics features, or restricted to mock sensor data for end-to-end learning.
Gym is cross-platform and compatible with most Reinforcement Learning frameworks implementing standard algorithms. For instance, Stable Baselines 3, Tianshou, or Rllib. Stable Baselines 3 and Tianshou use its counterpart Pytorch, and Rllib supports both. A few learning examples relying on those packages are also provided.
Pre-configured environments for some well-known toys models and reference robotics platforms are provided: cartpole, acrobot, pendulum, Ant, ANYmal, and Cassie, and Atlas.
Jiminy and Gym Jiminy support Linux, Mac and Windows, and is compatible with Python3.8+. Pre-compiled binaries are distributed on PyPi. They can be installed using
# For installing Jiminy python -m pip install --prefer-binary jiminy_py[meshcat,plot] # For installing Gym Jiminy python -m pip install --prefer-binary gym_jiminy[all]
Detailed installation instructions, including building from source, are available here.