  This paper extends recent work in interactive machine learning (IML) focused
on effectively incorporating human feedback. We show how control and feedback
signals complement each other in systems which model human reward. We
demonstrate that simultaneously incorporating human control and feedback
signals can improve interactive robotic systems' performance on a self-mirrored
movement control task where an RL-agent controlled right arm attempts to match
the preprogrammed movement pattern of the left arm. We illustrate the impact of
varying human feedback parameters on task performance by investigating the
probability of giving feedback on each time step and the likelihood of given
feedback being correct. We further illustrate that varying the temporal decay
with which the agent incorporates human feedback has a significant impact on
task performance. We found that smearing human feedback over time steps
improves performance and we show varying the probability of feedback at each
time step, and an increased likelihood of those feedbacks being 'correct' can
impact agent performance. We conclude that understanding latent variables in
human feedback is crucial for learning algorithms acting in human-machine
interaction domains.
