Non-Parametric Policy Gradients: A Unified Treatment of Propositional and Relational Domains

author: Kurt Driessens, KU Leuven
published: Aug. 4, 2008,   recorded: July 2008,   views: 3064


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.


Policy gradient approaches are a powerful instrument for learning how to interact with the environment.Existing approaches have focused on propositional and continuous domains only. Without extensive feature engineering, it is difficult -- if not impossible -- to apply them within structured domains, in which e.g. there is a varying number of objects and relations among them. In this paper, we describe a non-parametric policy gradient approach -- called NPPG -- that overcomes this limitation. The key idea is to apply Friedmann's gradient boosting: policies are represented as a weighted sum of regression models grown in an stage-wise optimization. Employing off-the-shelf regression learners, NPPG can deal with propositional, continuous, and relational domains in a unified way. Our experimental results show that it can even improve on established results.

See Also:

Download slides icon Download slides: icml08_driessens_npp_01.ppt (1.3┬áMB)

Help icon Streaming Video Help

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: