Environment Generation

Environment generation focuses on automatically creating diverse and complex virtual environments for training and evaluating artificial intelligence agents, particularly in reinforcement learning. Current research emphasizes using neural networks, such as neural cellular automata and large language models, often coupled with quality diversity algorithms or self-play techniques, to generate environments that are both challenging and scalable to larger problem sizes. This research aims to improve the efficiency and effectiveness of AI agent training by providing more realistic and diverse training data, ultimately leading to more robust and generally capable agents with improved performance in real-world applications.