  In this paper, we provide an overview of the existing methods for integrating
human advice into a Reinforcement Learning process. We first propose a taxonomy
of the different forms of advice that can be provided to a learning agent. We
then describe the methods that can be used for interpreting advice when its
meaning is not determined beforehand. Finally, we review different approaches
for integrating advice into the learning process.
