  This paper presents a recurrent hybrid model and training procedure for
task-oriented dialogue systems based on Deep Recurrent Q-Networks (DRQN). The
model copes with both tasks required for Dialogue Management: State Tracking
and Decision Making. It is based on modeling Human-Machine interaction into a
latent representation embedding an interaction context to guide the discussion.
The model achieves greater performance, learning speed and robustness than a
non-recurrent baseline. Moreover, results allow interpreting and validating the
policy evolution and the latent representations information-wise.
