  We investigate whether naturalistic emotional human feedback can be directly
exploited as a reward signal for training artificial agents via interactive
human-in-the-loop reinforcement learning. To answer this question, we devise an
experimental setting inspired by animal training, in which human test subjects
interactively teach an emulated drone agent their desired
command-action-mapping by providing emotional feedback on the drone's action
selections. We present a first empirical proof-of-concept study and analysis
confirming that human facial emotion expression can be directly exploited as
reward signal in such interactive learning settings. Thereby, we contribute
empirical findings towards more naturalistic and intuitive forms of
reinforcement learning especially designed for non-expert users.
