  Learning to produce efficient movement behaviour for humanoid robots from
scratch is a hard problem, as has been illustrated by the "Learning to run"
competition at NIPS 2017. The goal of this competition was to train a
two-legged model of a humanoid body to run in a simulated race course with
maximum speed. All submissions took a tabula rasa approach to reinforcement
learning (RL) and were able to produce relatively fast, but not optimal running
behaviour. In this paper, we demonstrate how data from videos of human running
(e.g. taken from YouTube) can be used to shape the reward of the humanoid
learning agent to speed up the learning and produce a better result.
Specifically, we are using the positions of key body parts at regular time
intervals to define a potential function for potential-based reward shaping
(PBRS). Since PBRS does not change the optimal policy, this approach allows the
RL agent to overcome sub-optimalities in the human movements that are shown in
the videos.
  We present experiments in which we combine selected techniques from the top
ten approaches from the NIPS competition with further optimizations to create
an high-performing agent as a baseline. We then demonstrate how video-based
reward shaping improves the performance further, resulting in an RL agent that
runs twice as fast as the baseline in 12 hours of training. We furthermore show
that our approach can overcome sub-optimal running behaviour in videos, with
the learned policy significantly outperforming that of the running agent from
the video.
