Skill Abstraction

Skill abstraction in reinforcement learning focuses on decomposing complex tasks into simpler, reusable sub-tasks (skills) to improve learning efficiency and generalization. Current research emphasizes learning these skills using various methods, including latent variable models (like transformers), diffusion models, and differentiable physics simulators, often incorporating hierarchical structures to manage long-horizon planning. This research is significant because it addresses the limitations of traditional reinforcement learning in handling complex, long-horizon tasks, paving the way for more robust and adaptable robotic systems and AI agents.