Course Description

This course will provide an introduction to, and comprehensive overview of, reinforcement learning. In general, reinforcement learning algorithms repeatedly answer the question "What should be done next?", and they can learn via trial and error to answer these questions even when there is no supervisor telling the algorithm what the correct answer would have been. Applications of reinforcement learning span across medicine, marketing, robotics, game playing, environmental applications, and dialogue systems, among many others.

Broad topics covered in this course will include: Markov decision processes, reinforcement learning algorithms (model-based / model-free, batch / online, value function based, actor-critics, policy gradient methods, etc.), hierarchical reinforcement learning, and connections to animal learning. Special topics may include ensuring the safety of reinforcement learning algorithms, theoretical reinforcement learning, and multi-agent reinforcement learning. This course will emphasize hands-on experience, and assignments will require the implementation and application of many of the algorithms discussed in class.

Syllabus, Notes, and Assignments

The syllabus, class notes, and assignments can all be found in one document linked below.

Download syllabus, notes, and assignments

LaTeX source for the class notes linked above can be found here.

Previous Year's Homework Assignments

The homework assignments from last year can be found here. The assignments this year will not be the same.