This course is to provide fundamental concepts and mathematical frameworks for reinforcement learning. Specific topics include Markov decision processes, tabular reinforcement learning, policy gradient methods, function approximation, and model-based methods. The course is technical and intended for advanced students with a strong mathematical background and programming skills. Emphasis will be placed on recent developments and principled approaches.