Reinforcement Learning Course - Georgia Tech

Abstract

Reinforcement learning is a popular and highly-developed approach to artificial intelligence with a wide range of applications. By integrating ideas from dynamic programming, machine learning, and psychology, reinforcement learning methods have enabled much better solutions to large-scale sequential decision problems than had previously been possible. This tutorial will cover Markov decision processes and approximate value functions as the formulation of the reinforcement learning problem, and temporal-difference learning, function approximation, and Monte Carlo methods as the principal solution methods.

The focus will be on the algorithms and their properties. Applications of reinforcement learning in robotics, game-playing, the web, and other areas will be highlighted. The main goal of the tutorial is to orient the AI researcher to the fundamentals and research topics in reinforcement learning, preparing them to evaluate possible applications and to access the literature efficiently.

Prerequisite Knowledge

We will assume familiarity with basic mathematical concepts such as conditional probabilities, expected values, derivatives, vectors and matrices.