Skill Discovery

Skill discovery in reinforcement learning focuses on enabling agents to autonomously learn diverse and useful behaviors without explicit reward signals, aiming to create a repertoire of reusable skills for complex tasks. Current research emphasizes methods that leverage large language models for semantic guidance, multi-modal distillation for lifelong learning, and contrastive learning or other information-theoretic objectives to maximize skill diversity and exploration while mitigating issues like catastrophic forgetting. This field is significant because it promises more efficient and adaptable AI agents, impacting robotics, autonomous systems, and other areas requiring robust, generalizable intelligence.