A Brief Tutorial on Reinforcement Learning
par/by Doina Precup
School of Computer Science
University McGill
Reinforcement learning is an approach for learning from interaction with
an environment. Instead of being explicitly taught what they should do,
reinforcement learning agents learn how to act by observing the
consequences of their actions. In this tutorial, I will briefly review
the mathematical framework of Markov Decision Processes (MDPs), on which
most reinforcement learning methods rely. I will then show how
temporal-difference learning methods can be derived from standard MDP
solution methods. I will discuss some of the current key problems in
reinforcement learning: the use of function approximation, dealing with
environments which are partially observable, and approaches for scaling up
reinforcement learning for large tasks.