  Human guidance is often desired in reinforcement learning to improve the
performance of the learning agent. However, human insights are often mere
opinions and educated guesses rather than well-formulated arguments. While
opinions are subject to uncertainty, e.g., due to partial informedness or
ignorance about a problem, they also emerge earlier than hard evidence can be
produced. Thus, guiding reinforcement learning agents by way of opinions offers
the potential for more performant learning processes, but comes with the
challenge of modeling and managing opinions in a formal way. In this article,
we present a method to guide reinforcement learning agents through opinions. To
this end, we provide an end-to-end method to model and manage advisors'
opinions. To assess the utility of the approach, we evaluate it with synthetic
(oracle) and human advisors, at different levels of uncertainty, and under
multiple advice strategies. Our results indicate that opinions, even if
uncertain, improve the performance of reinforcement learning agents, resulting
in higher rewards, more efficient exploration, and a better reinforced policy.
Although we demonstrate our approach through a two-dimensional topological
running example, our approach is applicable to complex problems with higher
dimensions as well.
