This course focuses on topics related to reinforcement learning. The course will cover making decisions under uncertainty, Markov decision processes, dynamic programming, temporal-difference learning, eligibility traces, value function approximation methods, Monte Carlo reinforcement learning methods, and the integration of learning and planning.