Model-Based Reinforcement Learning
published: Jan. 19, 2010, recorded: December 2009, views: 3435
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 model-based reinforcement learning, an agent uses its experience to construct a representation of the control dynamics of its environment. It can then predict the outcome of its actions and make decisions that maximize its learning and task performance. This tutorial will survey work in this area with an emphasis on recent results. Topics will include: Efficient learning in the PAC-MDP formalism, Bayesian reinforcement learning, models and linear function approximation, recent advances in planning.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !