Join us for an immersive introduction to reinforcement learning, where you'll discover what RL is and how it stands apart from supervised and unsupervised learning through real-world inspiration drawn from human trial-and-error, robotics, and gaming. 

Explore the essential building blocks of RL—including agents, environments, states, actions, and rewards—while delving into key concepts like the Markov Decision Process (MDP), the exploration versus exploitation trade-off, and the fundamentals of the Bellman Equation and dynamic programming.

 Learn about the various approaches within reinforcement learning, from model-free and model-based methods to value-based, policy-based, and hybrid techniques, and see how these ideas power innovative applications across engineering disciplines and even in the liberal arts, such as linguistics and Japanese studies.