CS234: Reinforcement Learning Winter 2025 (web.stanford.edu)

Stanford-style CS234: Reinforcement Learning (Winter 2025) published a full 11-week syllabus (Jan 6–Mar 23) outlining lectures, assignments, exams and project milestones. The course moves from fundamentals—tabular MDP planning, policy evaluation, Q‑learning and function approximation—into advanced topics via a three‑part policy search sequence, offline RL and imitation learning, distributional/policy optimization (DPO), and a multi‑week exploration module. Key checkpoints include three homework rounds, an in‑class midterm, a quiz, a project milestone, and a final poster session plus writeup. This schedule is notable for its practical, modern focus: several sessions on offline RL and DPO reflect the community’s interest in learning from fixed datasets, while the extended exploration blocks and a dedicated Monte Carlo Tree Search / AlphaGo lecture emphasize sample‑efficiency and planning. Guest lectures, in‑class assessments, and a project track signal a hands‑on pedagogy aimed at bridging theory and implementation. For researchers and practitioners, the syllabus highlights trending technical directions—function approximation with Q‑learning, policy optimization variants, offline methods and exploration strategies—that are central to current RL research and real‑world deployment.