Procedural Level Generation
Summary
Procedural Level Generation (PLG) is a technique used in artificial intelligence and game development to automatically create diverse and dynamic game environments. In the context of deep reinforcement learning (RL), PLG has emerged as a powerful tool to enhance the generalization capabilities of AI agents. By training RL models on procedurally generated levels rather than fixed environments, researchers have observed improved adaptability and performance across a wider range of scenarios. This approach helps mitigate overfitting issues commonly encountered when agents are trained on a single, static level. PLG can be fine-tuned to adjust difficulty levels in response to agent performance, potentially leading to more efficient learning processes. However, the effectiveness of PLG in preparing agents for human-designed levels depends significantly on the design and distribution of the level generators themselves. Researchers employ various analytical techniques, including dimensionality reduction and clustering, to evaluate and visualize the range of levels produced by PLG systems and assess their similarity to human-created content.