  In this work, we consider the problem of procedural content generation for
video game levels. Prior approaches have relied on evolutionary search (ES)
methods capable of generating diverse levels, but this generation procedure is
slow, which is problematic in real-time settings. Reinforcement learning (RL)
has also been proposed to tackle the same problem, and while level generation
is fast, training time can be prohibitively expensive. We propose a framework
to tackle the procedural content generation problem that combines the best of
ES and RL. In particular, our approach first uses ES to generate a sequence of
levels evolved over time, and then uses behaviour cloning to distil these
levels into a policy, which can then be queried to produce new levels quickly.
We apply our approach to a maze game and Super Mario Bros, with our results
indicating that our approach does in fact decrease the time required for level
generation, especially when an increasing number of valid levels are required.
