This lecture series introduces reinforcement learning fundamentals, covering Markov decision processes, dynamic programming, Monte Carlo methods, temporal‑difference learning, and function approximation. Delivered by leading researchers, it blends theory with practical insights. Learners gain a strong foundation to understand RL algorithms, their applications, and the challenges driving modern AI research.