Basics of Computational Reinforcement Learning
published: July 28, 2015, recorded: June 2015, views: 23074
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In machine learning, the problem of reinforcement learning is concerned with using experience gained through interacting with the world and evaluative feedback to improve a system’s ability to make behavioral decisions. This tutorial will introduce the fundamental concepts and vocabulary that underlie this field of study. It will also review recent advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology.
Download slides: rldm2015_littman_computational_reinforcement.pdf (10.7 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !