  Deep reinforcement learning (RL) has emerged as a promising approach for
autonomously acquiring complex behaviors from low level sensor observations.
Although a large portion of deep RL research has focused on applications in
video games and simulated control, which does not connect with the constraints
of learning in real environments, deep RL has also demonstrated promise in
enabling physical robots to learn complex skills in the real world. At the same
time,real world robotics provides an appealing domain for evaluating such
algorithms, as it connects directly to how humans learn; as an embodied agent
in the real world. Learning to perceive and move in the real world presents
numerous challenges, some of which are easier to address than others, and some
of which are often not considered in RL research that focuses only on simulated
domains. In this review article, we present a number of case studies involving
robotic deep RL. Building off of these case studies, we discuss commonly
perceived challenges in deep RL and how they have been addressed in these
works. We also provide an overview of other outstanding challenges, many of
which are unique to the real-world robotics setting and are not often the focus
of mainstream RL research. Our goal is to provide a resource both for
roboticists and machine learning researchers who are interested in furthering
the progress of deep RL in the real world.
