Speeding up relational reinforcement learning through the use of an incremental first order decision tree algorithm

Kurt Driessens, Jan Ramon, and Hendrik Blockeel. In L. De Raedt and P. Flach, editors, Proceedings of the 12th European Conference on Machine Learning, volume 2167 of Lecture Notes in Artificial Intelligence, pages 97--108. Springer-Verlag, September 2001. More behind this link.

Abstract

Relational reinforcement learning (RRL) is a learning technique that combines standard reinforcement learning with inductive logic programming to enable the learning system to exploit structural knowledge about the application domain. This paper discusses an improvement of the original RRL. We introduce a fully incremental first order decision tree learning algorithm TG and integrate this algorithm in the RRL system to form RRL-TG. We demonstrate the performance gain on similar experiments to those that were used to demonstrate the behaviour of the original RRL system.

BibTeX entry.

Other publications

K Driessens, kurtd@cs.kuleuven.ac.be,

J Ramon, janr@cs.kuleuven.ac.be,