* Reinforcement Learning Basics * Introduction to BURLAP * TD Lambda * Convergence of Value and Policy Iteration * Reward Shaping * Exploration * Generalization * Partially Observable MDPs * Options * Topics in Game Theory * Further Topics in RL Models
You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.