This lecture introduces reinforcement learning (RL), underscoring its vital role in advancing artificial intelligence and its varied applications in areas such as gaming, fusion science, and COVID testing. At its core, RL revolves around an automated agent that learns from experience to make informed decisions, focusing on optimization, managing delayed outcomes, exploration, and generalization. The lecture distinguishes RL from other machine learning methods, highlighting its unique advantages, particularly in situations with scarce data or when surpassing human performance is required. Notable examples like AlphaGo and ChatGPT demonstrate the power of RL in action, while the instructor also addresses some skepticism about RL's significance in the wider AI context. The session wraps up by outlining the course structure and content, promoting active learning strategies.
