  Deep reinforcement learning is a promising approach to learning policies in
uncontrolled environments that do not require domain knowledge. Unfortunately,
due to sample inefficiency, deep RL applications have primarily focused on
simulated environments. In this work, we demonstrate that the recent
advancements in machine learning algorithms and libraries combined with a
carefully tuned robot controller lead to learning quadruped locomotion in only
20 minutes in the real world. We evaluate our approach on several indoor and
outdoor terrains which are known to be challenging for classical model-based
controllers. We observe the robot to be able to learn walking gait consistently
on all of these terrains. Finally, we evaluate our design decisions in a
simulated environment.
