Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo
This provides a standardized toolkit for robotics researchers to evaluate and compare reinforcement learning algorithms in simulation.
The authors extended OpenAI Gym for robotics by integrating ROS and Gazebo, creating a benchmarking system that allows comparison of reinforcement learning techniques like Q-Learning and Sarsa under consistent virtual conditions.
This paper presents an extension of the OpenAI Gym for robotics using the Robot Operating System (ROS) and the Gazebo simulator. The content discusses the software architecture proposed and the results obtained by using two Reinforcement Learning techniques: Q-Learning and Sarsa. Ultimately, the output of this work presents a benchmarking system for robotics that allows different techniques and algorithms to be compared using the same virtual conditions.