PCGRL: Procedural Content Generation via Reinforcement Learning
This addresses the challenge of automated level design for game developers, offering a novel approach that is incremental in applying RL to a specific domain.
The paper tackles the problem of procedural content generation in games by framing level design as a sequential task and using reinforcement learning to train agents, resulting in a fast generator that works with few or no training examples.
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. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes and apply these to three game environments.