  For robots to coexist with humans in a social world like ours, it is crucial
that they possess human-like social interaction skills. Programming a robot to
possess such skills is a challenging task. In this paper, we propose a
Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like
interaction skills through a trial and error method. This paper aims to develop
a robot that gathers data during its interaction with a human and learns human
interaction behaviour from the high-dimensional sensory information using
end-to-end reinforcement learning. This paper demonstrates that the robot was
able to learn basic interaction skills successfully, after 14 days of
interacting with people.
