  Rather than programming, training allows robots to achieve behaviors that
generalize better and are capable to respond to real-world needs. However, such
training requires a big amount of experimentation which is not always feasible
for a physical robot. In this work, we present robot_gym, a framework to
accelerate robot training through simulation in the cloud that makes use of
roboticists' tools, simplifying the development and deployment processes on
real robots. We unveil that, for simple tasks, simple 3DoF robots require more
than 140 attempts to learn. For more complex, 6DoF robots, the number of
attempts increases to more than 900 for the same task. We demonstrate that our
framework, for simple tasks, accelerates the robot training time by more than
33% while maintaining similar levels of accuracy and repeatability.
