Scalable procedural content generation via transfer reinforcement learning

Abstract

Procedural Content Generation algorithms that make use of Machine Learning have garnered significant attention from the general public due to their ability to generate text, images, or video content that is often indistinguishable from human-crafted artworks. These achievements typically necessitate a substantial quantity of data, which may be scarce in specialized domains such as game-level design. One machine learning technique, Reinforcement Learning (RL), can be employed to learn from trial and error to address this scarcity. However, the RL training process requires a substantial time investment and may prove unsuccessful in the case of sparse reward structures. This paper empirically demonstrates the efficacy of curriculum learning as a viable solution to address scalability issues inherent to learning more complex tasks. Instead of trying to learn the whole space from scratch, we employ transfer learning on a curriculum from small to larger levels. We empirically validated this in a 3D vector-based environment, where the objective is to generate free-form tracks that facilitate a rider to move between designated points in the same vain as the flash game hit “Linerider”.