  The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and
building point for much deep reinforcement learning research. However,
replicating results for complex systems is often challenging since original
scientific publications are not always able to describe in detail every
important parameter setting and software engineering solution. In this paper,
we present results from our work reproducing the results of the DQN paper. We
highlight key areas in the implementation that were not covered in great detail
in the original paper to make it easier for researchers to replicate these
results, including termination conditions and gradient descent algorithms.
Finally, we discuss methods for improving the computational performance and
provide our own implementation that is designed to work with a range of
domains, and not just the original Arcade Learning Environment [Bellemare et
al., 2013].
