Section outline

  • Lesson Goal: Introduce inverse reinforcement learning (IRL) – how AI can learn what to optimize by observing human behavior – and why this is key for aligning AI with human goals.

    • ·         Micro-topic 21.1: Understanding Reinforcement Learning Basics (Goal: Ensure students recall how standard reinforcement learning works and key terms like agent, reward, and policy)

    • L21 MT1 FREE RL Basics SCORM12 SCORM package
      • Micro-topic 21.2: Why Do We Need Inverse Reinforcement Learning? (Goal: Illustrate why we might not want to hard-code reward functions and instead learn them from human behavior)
    • L21 MT2 FREE Why IRL SCORM12 SCORM package
      • Micro-topic 21.3: How Inverse Reinforcement Learning Works (Goal: Describe the mechanics of IRL – what the algorithm takes in and produces, and the challenges in inferring the “true” reward)
    • L21 MT3 FREE How IRL Works SCORM12 SCORM package
      • Micro-topic 21.4: Examples and Challenges of Learning from Humans (Goal: Give a concrete example of IRL in action and discuss practical challenges like imperfect data or suboptimal experts)
    • L21 MT4 FREE Examples Challenges SCORM12 SCORM package
      • Micro-topic 21.5: Real-World Uses of Inverse RL (Goal: Highlight practical applications of IRL and how learning from people can give AI a competitive edge in the job war against AI – by aligning AI tools to human strategies and values)
    • L21 MT5 FREE Real World Uses IRL SCORM12 SCORM package