  Deep reinforcement learning has proven to be a great success in allowing
agents to learn complex tasks. However, its application to actual robots can be
prohibitively expensive. Furthermore, the unpredictability of human behavior in
human-robot interaction tasks can hinder convergence to a good policy. In this
paper, we present an architecture that allows agents to learn models of
stochastic environments and use them to accelerate learning. We descirbe how an
environment model can be learned online and used to generate synthetic
transitions, as well as how an agent can leverage these synthetic data to
accelerate learning. We validate our approach using an experiment in which a
robotic arm has to complete a task composed of a series of actions based on
human gestures. Results show that our approach leads to significantly faster
learning, requiring much less interaction with the environment. Furthermore, we
demonstrate how learned models can be used by a robot to produce optimal plans
in real world applications.
