  Current robot platforms available for research are either very expensive or
unable to handle the abuse of exploratory controls in reinforcement learning.
We develop RealAnt, a minimal low-cost physical version of the popular `Ant'
benchmark used in reinforcement learning. RealAnt costs only $\sim$350 EUR
(\$410) in materials and can be assembled in less than an hour. We validate the
platform with reinforcement learning experiments and provide baseline results
on a set of benchmark tasks. We demonstrate that the RealAnt robot can learn to
walk from scratch from less than 10 minutes of experience. We also provide
simulator versions of the robot (with the same dimensions, state-action spaces,
and delayed noisy observations) in the MuJoCo and PyBullet simulators. We
open-source hardware designs, supporting software, and baseline results for
educational use and reproducible research.
