Reinforcement Learning

Catalog description

DS 551 Reinforcement Learning (3 credits) Reinforcement Learning (RL) is an area of machine learning concerned with how agents take actions in an environment with a goal of maximizing some notion of “cumulative reward”. The problem, due to its generality, is studied in many disciplines, and applied in many domains, including robotics and industrial automation, marketing, education and training, health and medicine, text, speech, dialog systems, finance, among many others. In this course, we will cover topics including: Markov decision processes, reinforcement learning algorithms, value function approximation, actor-critics, policy gradient methods, representations for reinforcement learning (including deep learning), and inverse reinforcement learning. The course project(s) will require the implementation and application of many of the algorithms discussed in class. Prerequisites: Machine Learning (CS 539), statistical learning at the level of DS 502/MA 543, and programming skills at the level of CS 5007.