  This paper presents 'SimpleDS', a simple and publicly available dialogue
system trained with deep reinforcement learning. In contrast to previous
reinforcement learning dialogue systems, this system avoids manual feature
engineering by performing action selection directly from raw text of the last
system and (noisy) user responses. Our initial results, in the restaurant
domain, show that it is indeed possible to induce reasonable dialogue behaviour
with an approach that aims for high levels of automation in dialogue control
for intelligent interactive agents.
