Hierarchical Model-Based Reinforcement Learning
published: Aug. 4, 2008, recorded: July 2008, views: 5166
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.
Hierarchical decomposition promises to help scale reinforcement learning algorithms naturally to real-world problems by exploiting their underlying structure. Model-based algorithms, which provided the first finite-time convergence guarantees for reinforcement learning, may also play an important role in coping with the relative scarcity of data in large environments. In this paper, we introduce an algorithm that fully integrates modern hierarchical and model-learning methods in the standard reinforcement learning setting. Our algorithm, R-maxq, inherits the efficient model-based exploration of the R-max algorithm and the opportunities for abstraction provided by the MAXQ framework. We analyze the sample complexity of our algorithm, and our experiments in a standard simulation environment illustrate the advantages of combining hierarchies and models.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !