Introduction to Modern Reinforcement Learning
This course provides a comprehensive, hands-on introduction to reinforcement learning (RL), bridging foundational theory with state-of-the-art methods used in modern AI systems. Students will learn how agents learn from interaction through the lens of Markov Decision Processes, value functions, and policy optimization. Beginning with classical tabular methods, the course progresses through deep RL architectures (DQN, PPO), human-in-the-loop learning (RLHF), and advanced topics.
Weekly labs emphasize implementation and experimentation and four open-ended challenges integrate concepts like safety, generalization, and alignment. By the end of the semester, students will be equipped to design, train, and critically evaluate RL agents.