Unsupervised Environment Design

Unsupervised Environment Design (UED) aims to automatically generate training environments for reinforcement learning agents, improving their robustness and generalization to unseen situations. Current research focuses on developing algorithms that efficiently and effectively sample diverse environments, often employing minimax regret strategies or generative models to create challenging yet informative training curricula. This work is significant because it addresses the critical bottleneck of environment shaping in reinforcement learning, potentially leading to more efficient and reliable training of agents for real-world applications like robotics and autonomous systems.