JdeRobot/RL-Studio

Robotic library for the training of Reinforcement Learning algorithms

40
/ 100
Emerging

This platform helps roboticists and autonomous vehicle developers train and test reinforcement learning algorithms for robotic applications. It takes sensor data from simulations (like autonomous driving scenarios in Carla or Gazebo) and applies various algorithms to output a trained model that can control a robot's actuators. The end-user is a roboticist or engineer focused on developing autonomous systems.

No commits in the last 6 months.

Use this if you need a flexible framework to train, retrain, or validate reinforcement learning models for robotic control within simulated environments, especially for autonomous driving or similar tasks.

Not ideal if you are looking for a simple, out-of-the-box solution for non-robotic reinforcement learning applications or do not have experience with ROS and robotic simulators.

robotics autonomous-vehicles reinforcement-learning-engineering sim-to-real-transfer robot-control
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 17 / 25

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Stars

31

Forks

8

Language

Python

License

GPL-3.0

Last pushed

Jul 20, 2024

Commits (30d)

0

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