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In this work, we consider the problem of procedural content generation forvideo game levels. Prior approaches have relied on evolutionary search (ES)methods capable of generating diverse levels, but this generation procedure isslow, which is problematic in real-time settings. Reinforcement learning (RL)has also been proposed to tackle the same problem, and while level generationis fast, training time can be prohibitively expensive. We propose a frameworkto tackle the procedural content generation problem that combines the best ofES and RL. In particular, our approach first uses ES to generate a sequence oflevels evolved over time, and then uses behaviour cloning to distil theselevels 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 resultsindicating that our approach does in fact decrease the time required for levelgeneration, especially when an increasing number of valid levels are required.

Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content