  Humans are spectacular reinforcement learners, constantly learning from and
adjusting to experience and feedback. Unfortunately, this doesn't necessarily
mean humans are fast learners. When tasks are challenging, learning can become
unacceptably slow. Fortunately, humans do not have to learn tabula rasa, and
learning speed can be greatly increased with learning aids. In this work we
validate a new type of learning aid -- reward shaping for humans via inverse
reinforcement learning (IRL). The goal of this aid is to increase the speed
with which humans can learn good policies for specific tasks. Furthermore this
approach compliments alternative machine learning techniques such as safety
features that try to prevent individuals from making poor decisions. To achieve
our results we first extend a well known IRL algorithm via kernel methods.
Afterwards we conduct two human subjects experiments using an online game where
players have limited time to learn a good policy. We show with statistical
significance that players who receive our learning aid are able to approach
desired policies more quickly than the control group.
