Procedural Content Generation using Reinforcement Learning and Entropy Measure as Feedback

In this work, we investigate how we can approach procedural content generation with reinforcement learning and mixed-initiative design. A second question discussed here is how we can use metrics to evaluate the diversity of the generated level. Our proposal has as its main hypothesis to use scenario models, provided by an expert human level designer specialist, for the reinforcement learning agents in order to generate new scenarios. The levels provided by the specialist are separated into segments or blocks that are used to compose the new scenario structures. Also, a new reward function based on the use of entropy was proposed to measure the diversity of the generated scenarios. Initially, we trained our model for three different 2D Dungeon crawlers game environments. We analyzed our results through the value of the entropy, and it shows that our approach can generate wide levels with a diversity of segments.