Stanford CS234 - Reinforcement Learning - Spring 2024

Explore the fundamentals and advanced concepts of reinforcement learning through this comprehensive Stanford University course taught by Professor Emma Brunskill. Master the core principles of autonomous decision-making systems that learn to optimize their behavior through interaction with environments. Begin with an introduction to reinforcement learning and tabular MDP planning, then progress through policy evaluation techniques and Q-learning with function approximation. Delve into policy search methods across multiple lectures, covering various approaches and implementations. Examine offline reinforcement learning strategies and their applications, including a guest lecture on Direct Preference Optimization (DPO) by leading researchers. Investigate exploration strategies and techniques for balancing exploitation versus exploration in uncertain environments. Study multi-agent systems and game-playing scenarios where multiple learning agents interact. Conclude with discussions on value alignment, addressing how to ensure AI systems pursue intended objectives. Gain practical experience through coding assignments implementing both classical and deep reinforcement learning algorithms, preparing you for applications in robotics, game playing, consumer modeling, and healthcare domains.