  Uncertainty is ubiquitous in games, both in the agents playing games and
often in the games themselves. Working with uncertainty is therefore an
important component of successful deep reinforcement learning agents. While
there has been substantial effort and progress in understanding and working
with uncertainty for supervised learning, the body of literature for
uncertainty aware deep reinforcement learning is less developed. While many of
the same problems regarding uncertainty in neural networks for supervised
learning remain for reinforcement learning, there are additional sources of
uncertainty due to the nature of an interactable environment. In this work, we
provide an overview motivating and presenting existing techniques in
uncertainty aware deep reinforcement learning. These works show empirical
benefits on a variety of reinforcement learning tasks. This work serves to help
to centralize the disparate results and promote future research in this area.
