Stanford CS234 Reinforcement Learning I Value Alignment I 2024 I Lecture 16

In this final lecture of CS234, we revisit the course material along with insights from the recent quiz. We address common questions that students have about Proximal Policy Optimization (PPO), the alignment problem discussed by a guest lecturer, Monte Carlo Tree Search (MCTS), and the theoretical aspects of various reinforcement learning algorithms. To wrap up, we reflect on the motivating examples introduced in the first lecture—such as AlphaTensor, plasma control, and COVID testing. Students are encouraged to approach these real-world challenges with the knowledge they've gained, considering the complexities and possibilities in applying reinforcement learning. We also stress the need to find a balance between computational demands and data efficiency while highlighting ongoing challenges in this dynamic field.