  Reinforcement Learning (RL) can be extremely effective in solving complex,
real-world problems. However, injecting human knowledge into an RL agent may
require extensive effort and expertise on the human designer's part. To date,
human factors are generally not considered in the development and evaluation of
possible RL approaches. In this article, we set out to investigate how
different methods for injecting human knowledge are applied, in practice, by
human designers of varying levels of knowledge and skill. We perform the first
empirical evaluation of several methods, including a newly proposed method
named SASS which is based on the notion of similarities in the agent's
state-action space. Through this human study, consisting of 51 human
participants, we shed new light on the human factors that play a key role in
RL. We find that the classical reward shaping technique seems to be the most
natural method for most designers, both expert and non-expert, to speed up RL.
However, we further find that our proposed method SASS can be effectively and
efficiently combined with reward shaping, and provides a beneficial alternative
to using only a single speedup method with minimal human designer effort
overhead.
