  For a safe, natural and effective human-robot social interaction, it is
essential to develop a system that allows a robot to demonstrate the
perceivable responsive behaviors to complex human behaviors. We introduce the
Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits
human-like social interaction skills after 14 days of interacting with people
in an uncontrolled real world. Each and every day during the 14 days, the
system gathered robot interaction experiences with people through a
hit-and-trial method and then trained the MDARQN on these experiences using
end-to-end reinforcement learning approach. The results of interaction based
learning indicate that the robot has learned to respond to complex human
behaviors in a perceivable and socially acceptable manner.
