CMPUT 365 - Reinforcement Learning

Overview

This course provides an introduction to reinforcement learning intelligence, which focuses on the study and design of agents that interact with a complex, uncertain world to achieve a goal. We will emphasize agents that can make near-optimal decisions in a timely manner with incomplete information and limited computational resources. The course will cover Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the concept of mind and briefly touch on perspectives from psychology, neuroscience, and philosophy.

The course will use a recently created MOOC on Reinforcement Learning, created by the Instructors of the Fall 2019 course. Much of the lecture material and assignments will come from the MOOC. In-class time will be largely spent on discussion and thinking about the material, with some supplementary lectures.

Objectives

By the end of the course, you will have a solid grasp of the main ideas in reinforcement learning, which is the primary approach to statistical decision-making. Any student who understands the material in this course will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses (in particular CMPUT 609: Reinforcement Learning II, CMPUT 652: Reinforcement Learning with Robots, and CMPUT 607: Applied Reinforcement Learning), or to apply AI tools and ideas to real-world problems. That person will be able to apply these tools and ideas in novel situations - eg, to determine whether the methods apply to this situation, and if so, which will work most effectively. They will also be able to assess claims made by others, with respect to both software products and general frameworks, and also be able to appreciate some new research results.

Course Work

• Assignments
• Projects
• Participation
• Midterms
• Final Exam