Reinforcement Learning is the area of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards. You can apply Reinforcement Learning to robot control, chess, backgammon, checkers, and other activities that a software agent can learn. Reinforcement Learning uses behaviorist psychology in order to achieve reward maximization.
You can expect to spend 8-10 hours per week on this course.
Series Information: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.
- Machine Learning 1: Supervised Learning
- Machine Learning 2: Unsupervised Learning
- Machine Learning 3: Reinforcement Learning (this course)
If you are new to Machine Learning, we recommend you take these 3 courses in order.
The entire series is taught as a lively and rigorous dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).
You will learn about Reinforcement Learning, the field of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards.
In this course, you will gain an understanding of topics and methods in Reinforcement Learning, including Markov Decision Processes and Game Theory. You will gain experience implementing Reinforcement Learning techniques in a final project.