Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning

Refine reusable skills from noisy demonstrations for reliable adaptation to new, long-horizon tasks.

SISL separates high-level task execution from skill improvement. It prioritizes useful trajectories through maximum return relabeling and repeatedly refines the skill library, improving adaptation when demonstrations are noisy or suboptimal.