Reinforcement Learning

Catalog description

Reinforcement learning (RL) is a powerful paradigm for autonomous systems to learn to make decisions, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will provide a solid introduction to the field of reinforcement learning and students will learn about the core concepts, design principles, widely used algorithms, RL with functional approximation, and generalization of RL. Through a combination of lectures, literature review, and written and coding assignments, students will become well versed in key ideas and techniques for RL including basics of reinforcement learning as well as deep reinforcement learning — an area that combines deep learning techniques with reinforcement learning.