  In this work, we investigate the application of Reinforcement Learning to two
well known decision dilemmas, namely Newcomb's Problem and Prisoner's Dilemma.
These problems are exemplary for dilemmas that autonomous agents are faced with
when interacting with humans. Furthermore, we argue that a Newcomb-like
formulation is more adequate in the human-machine interaction case and
demonstrate empirically that the unmodified Reinforcement Learning algorithms
end up with the well known maximum expected utility solution.
