PCGRL: Procedural Content Generation via Reinforcement Learning

Understand

We investigate how reinforcement learning can be used to train level-designing agents.

• This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned.
• By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized.
• This approach can be used when few or no examples exist to train from, and the trained generator is very fast.