Lecture notes in computer science vol:2167 pages:97-108
European Conference on Machine Learning location:Freiburg, Germany date:September 3-7, 2001
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.