Skill Prior

Skill priors represent a crucial advancement in reinforcement learning, aiming to accelerate learning and improve generalization by leveraging previously acquired knowledge. Current research focuses on developing methods to learn and effectively combine diverse skill priors, often employing generative models like diffusion models or leveraging latent spaces to represent skills, and integrating them into hierarchical reinforcement learning frameworks. This approach shows promise in enabling robots to perform complex, long-horizon tasks, particularly in manipulation and assembly, by efficiently transferring knowledge across similar but not identical environments and improving data efficiency and safety. The resulting improvements in learning speed and generalization have significant implications for robotics and other fields requiring adaptive intelligent agents.