Article

Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner

Authors:

Kurt Driessens,

Jan Ramon,

Hendrik BlockeelAuthors Info & Claims

EMCL '01: Proceedings of the 12th European Conference on Machine Learning

Pages 97 - 108

Published: 05 September 2001 Publication History

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.

References

[1]

H. Blockeel and L. De Raedt. Top-down induction of first order logical decision trees. Artificial Intelligence, 101(1-2):285-297, June 1998.

Crossref

Google Scholar

[2]

H. Blockeel, L. De Raedt, and J. Ramon. Top-down induction of clustering trees. In Proceedings of the 15th International Conference on Machine Learning, pages 55-63, 1998. http://www.cs.kuleuven.ac.be/~ml/PS/ML98-56.ps.

Crossref

Google Scholar

[3]

H. Blockeel, B. Demoen, L. Dehaspe, G. Janssens, J. Ramon, and H. Vandecasteele. Executing query packs in ILP. In J. Cussens and A. Frisch, editors, Proceedings of the 10th International Conference in Inductive Logic Programming, volume 1866 of Lecture Notes in Artificial Intelligence, pages 60-77, London, UK, July 2000. Springer.

Google Scholar

[4]

David Chapman and Leslie P. Kaelbling. Input generalization in delayed reinforcement learning: An algorithm and performance comparisions. In Proceedings of the International Joint Conference on Artificial Intelligence, 1991.

Crossref

Google Scholar

[5]

S. Džeroski, L. De Raedt, and K. Driessens. Relational reinforcement learning. Machine Learning, 43:7-52, 2001.

Crossref

Google Scholar

[6]

L. Kaelbling, M. Littman, and A. Moore. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4:237-285, 1996.

Crossref

Google Scholar

[7]

K. Kersting and L. De Raedt. Bayesian logic programs. In Proceedings of the tenth international conference on inductive logic programming, work in progress track, 2000.

Google Scholar

[8]

P. Langley. Elements of Machine Learning. Morgan Kaufmann, 1996.

Crossref

Google Scholar

[9]

T. Mitchell. Machine Learning. McGraw-Hill, 1997.

Crossref

Google Scholar

[10]

R. Sutton and A. Barto. Reinforcement Learning: an introduction. The MIT Press, Cambridge, MA, 1998.

Crossref

Google Scholar

Cited By

View all

Driessens KDžeroski S(2019)Integrating Guidance into Relational Reinforcement LearningMachine Language10.1023/B:MACH.0000039779.47329.3a57:3(271-304)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1023/B%3AMACH.0000039779.47329.3a
Ikonomovska EDžeroski S(2011)Regression on evolving multi-relational data streamsProceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop10.1145/1966874.1966875(1-7)Online publication date: 25-Mar-2011
https://dl.acm.org/doi/10.1145/1966874.1966875
Rodrigues CGérard PRouveirol CSoldano H(2011)Active learning of relational action modelsProceedings of the 21st international conference on Inductive Logic Programming10.1007/978-3-642-31951-8_26(302-316)Online publication date: 31-Jul-2011
https://dl.acm.org/doi/10.1007/978-3-642-31951-8_26
Show More Cited By

Index Terms

Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner

Recommendations

Speeding up relational reinforcement learning through the use of an incremental first order decision tree learner
ECML'01: Proceedings of the 12th European Conference on Machine Learning

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 ...
Relational Reinforcement Learning

Relational reinforcement learning is presented, a learning technique that combines reinforcement learning with relational learning or inductive logic programming. Due to the use of a more expressive representation language to represent states, actions ...
Relational Reinforcement Learning
EASSS '01: Selected Tutorial Papers from the 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School on Multi-Agent Systems and Applications

This paper presents an introduction to reinforcement learning and relational reinforcement learning at a level to be understood by students and researchers with different backgrounds.It gives an overview of the fundamental principles and techniques of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

EMCL '01: Proceedings of the 12th European Conference on Machine Learning

September 2001

611 pages

ISBN:3540425365

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 05 September 2001

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Driessens KDžeroski S(2019)Integrating Guidance into Relational Reinforcement LearningMachine Language10.1023/B:MACH.0000039779.47329.3a57:3(271-304)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1023/B%3AMACH.0000039779.47329.3a
Ikonomovska EDžeroski S(2011)Regression on evolving multi-relational data streamsProceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop10.1145/1966874.1966875(1-7)Online publication date: 25-Mar-2011
https://dl.acm.org/doi/10.1145/1966874.1966875
Rodrigues CGérard PRouveirol CSoldano H(2011)Active learning of relational action modelsProceedings of the 21st international conference on Inductive Logic Programming10.1007/978-3-642-31951-8_26(302-316)Online publication date: 31-Jul-2011
https://dl.acm.org/doi/10.1007/978-3-642-31951-8_26
Rodrigues CGérard PRouveirol C(2010)Incremental learning of relational action models in noisy environmentsProceedings of the 20th international conference on Inductive logic programming10.5555/2022735.2022761(206-213)Online publication date: 27-Jun-2010
https://dl.acm.org/doi/10.5555/2022735.2022761
Ponsen MCroonenborghs TTuyls KRamon JDriessens KSierra CCastelfranchi CDecker KSichman J(2009)Learning with whom to communicate using relational reinforcement learningProceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 210.5555/1558109.1558222(1221-1222)Online publication date: 10-May-2009
https://dl.acm.org/doi/10.5555/1558109.1558222
Kersting KDriessens KCohen WMcCallum ARoweis S(2008)Non-parametric policy gradientsProceedings of the 25th international conference on Machine learning10.1145/1390156.1390214(456-463)Online publication date: 5-Jul-2008
https://dl.acm.org/doi/10.1145/1390156.1390214
Walker TTorrey LShavlik JMaclin R(2007)Building relational world models for reinforcement learningProceedings of the 17th international conference on Inductive logic programming10.5555/1793494.1793524(280-291)Online publication date: 19-Jun-2007
https://dl.acm.org/doi/10.5555/1793494.1793524
Croonenborghs TDriessens KBruynooghe M(2007)Learning relational options for inductive transfer in relational reinforcement learningProceedings of the 17th international conference on Inductive logic programming10.5555/1793494.1793509(88-97)Online publication date: 19-Jun-2007
https://dl.acm.org/doi/10.5555/1793494.1793509
Dabney WMcGovern A(2007)Utile distinctions for relational reinforcement learningProceedings of the 20th international joint conference on Artifical intelligence10.5555/1625275.1625394(738-743)Online publication date: 6-Jan-2007
https://dl.acm.org/doi/10.5555/1625275.1625394
Croonenborghs TRamon JBlockeel HBruynooghe M(2007)Online learning and exploiting relational models in reinforcement learningProceedings of the 20th international joint conference on Artifical intelligence10.5555/1625275.1625392(726-731)Online publication date: 6-Jan-2007
https://dl.acm.org/doi/10.5555/1625275.1625392
Show More Cited By

Abstract

References

Cited By

Index Terms

Recommendations