Robot Reinforcement Learning is becoming more and more popular however setting up the infrastructure to do reinforcement learning with popular tools like Gazebo and ROS can take quite a bit of time, specially if you have to do it in tenths of servers to automate the learning process in a robot.
This article will walk you through the process of how to use the gym-gazebo toolbox for reinforcement learning via a Docker container that has everything cooked already.
## Getting gym-gazebo as a Docker container
Assuming that you’ve got Docker installed in your system, here’s what you need to do:
``` docker pull erlerobotics/gym-gazebo:latest docker run -it gym-gazebo
That easy! The overall process of fetching the container takes about 15 minutes but afterwards you can deploy as many as you want with a single command line. Depending on your simulations (typically when using plugins like cameras), you might need to set a fake screen on your server/dev. machine. Here’s how to do it:
``` xvfb-run -s “-screen 0 1400x900x24” bash
## Getting a local front-end
Sometimes, you may want to supervise the learning process using
gzclient. Assuming that the docker container is running locally, here’s what you’d do:
export GAZEBO_MASTER_IP=$(sudo docker inspect –format ‘’ “id of running container”) export GAZEBO_MASTER_URI=$GAZEBO_MASTER_IP:11345 gzclient