Chapter 1

From AlphaZero to RLHF

Start with the central idea of RL: an agent learns by acting, observing consequences, and improving future decisions. AlphaZero, Gato, and RLHF provide concrete anchors for the rest of the course.

• 1. Why Reinforcement Learning Matters→
• 2. Agent-Environment Loop→
• 3. What Is an Agent?→
• 4. States, Actions, Rewards, and Policies→
• 5. AlphaZero as a Motivating Example→
• 6. Gato as RL-Generated Data→
• 7. RLHF as Modern RL→