Basis Function Construction for Hierarchical Reinforcement Learning

author: Sarah Osentoski, Department of Computer Science, University of Massachusetts Amherst
published: Aug. 26, 2009,   recorded: June 2009,   views: 3488

Related Open Educational Resources

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

This paper introduces an approach to automatic basis function construction for Hierarchical Reinforcement Learning (HRL) tasks. We describe some considerations that arise when constructing basis functions for multilevel task hierarchies. We extend previous work on using Laplacian bases for value function approximation to situations where the agent is provided with a multi-level action hierarchy. We experimentally evaluate these techniques on the Taxi domain.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: