EE 7183 - Reinforcement Learning

This course will provide a comprehensive introduction to reinforcement learning as an approach to artificial intelligence, emphasizing the design of complete agents interacting with stochastic, incompletely known environments. Reinforcement learning has adapted key ideas from machine learning, operations research, psychology, and neuroscience to produce some strikingly successful engineering applications. The focus is on algorithms for learning what actions to take, and when to take them, so as to optimize long-term performance. This may involve sacrificing immediate reward to obtain greater reward in the long-term or just to obtain more information about the environment. The course will cover Markov decision processes, dynamic programming, temporal-difference learning, Monte Carlo reinforcement learning methods, eligibility traces, the role of function approximation, and the integration of learning and planning. The course will emphasize the development of intuition relating the mathematical theory of reinforcement learning to the design of human-level artificial intelligence. “Reinforcement learning is learning what to do—how to map situations to actions—so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation and, through that, all subsequent rewards. These two characteristics—trial-and-error search and delayed reward—are the two most important distinguishing features of reinforcement learning.” This course will prepare you to study computational principles and hardware organization of what we mean by intelligence and goal-directed behavior. How to motivate machine to act on its own, yet to satisfy a desired objective? How machine interaction with environment leads to better behavior, better understanding, and success in its mission? What are the computational issues in doing this efficiently and in real time?

Requisites

Credit Hours: 3.0
Repeat/Retake Information: May not be retaken.

Lecture/Lab Hours: 3.0 lecture

Eligible grades: A-F,WP,WF,FN,FS,AU,I