Provides a broad introduction to reinforcement learning and sequential decision-making methods. Topics include: Foundations of reinforcement learning: Markov decision processes, Bellman equations, dynamic programming, policy evaluation and improvement, Monte Carlo methods, temporal-difference learning, Q-learning, and SARSA. Function approximation and deep reinforcement learning: value function approximation, deep Q-networks, policy gradient methods, actor-critic architectures, and model-based reinforcement learning. Additional topics may include multi-agent reinforcement learning, imitation learning, inverse reinforcement learning, exploration versus exploitation strategies, hierarchical reinforcement learning, and safe reinforcement learning. The course will also discuss recent applications of reinforcement learning, such as robotics, autonomous navigation, game playing, intelligent control systems, resource optimization, adaptive decision-making systems, and applications to training of large language models.