  Human-computer interactive systems that rely on machine learning are becoming
paramount to the lives of millions of people who use digital assistants on a
daily basis. Yet, further advances are limited by the availability of data and
the cost of acquiring new samples. One way to address this problem is by
improving the sample efficiency of current approaches. As a solution path, we
present a model-based reinforcement learning algorithm for an interactive
dialogue task. We build on commonly used actor-critic methods, adding an
environment model and planner that augments a learning agent to learn the model
of the environment dynamics. Our results show that, on a simulation that mimics
the interactive task, our algorithm requires 70 times fewer samples, compared
to the baseline of commonly used model-free algorithm, and demonstrates 2~times
better performance asymptotically. Moreover, we introduce a novel contribution
of computing a soft planner policy and further updating a model-free policy
yielding a less computationally expensive model-free agent as good as the
model-based one. This model-based architecture serves as a foundation that can
be extended to other human-computer interactive tasks allowing further advances
in this direction.
