  Reinforcement Learning AI commonly uses reward/penalty signals that are
objective and explicit in an environment -- e.g. game score, completion time,
etc. -- in order to learn the optimal strategy for task performance. However,
Human-AI interaction for such AI agents should include additional reinforcement
that is implicit and subjective -- e.g. human preferences for certain AI
behavior -- in order to adapt the AI behavior to idiosyncratic human
preferences. Such adaptations would mirror naturally occurring processes that
increase trust and comfort during social interactions. Here, we show how a
hybrid brain-computer-interface (hBCI), which detects an individual's level of
interest in objects/events in a virtual environment, can be used to adapt the
behavior of a Deep Reinforcement Learning AI agent that is controlling a
virtual autonomous vehicle. Specifically, we show that the AI learns a driving
strategy that maintains a safe distance from a lead vehicle, and most novelly,
preferentially slows the vehicle when the human passengers of the vehicle
encounter objects of interest. This adaptation affords an additional 20\%
viewing time for subjectively interesting objects. This is the first
demonstration of how an hBCI can be used to provide implicit reinforcement to
an AI agent in a way that incorporates user preferences into the control
system.
