Elements of machine learning
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Input generalization in delayed reinforcement learning: an algorithm and performance comparisons
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Top-down induction of first-order logical decision trees
Artificial Intelligence
Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling
ECML '07 Proceedings of the 18th European conference on Machine Learning
A Learning Classifier System Approach to Relational Reinforcement Learning
Learning Classifier Systems
Regression on evolving multi-relational data streams
Proceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop
Incremental learning of relational action models in noisy environments
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Information Sciences: an International Journal
Multi-agent relational reinforcement learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Expert Systems with Applications: An International Journal
Active learning of relational action models
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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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.