Utile distinctions for relational reinforcement learning

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 738-743

  Dabney, William ^a   McGovern, Amy ^a  

^a University of Oklahoma   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHANGING ENVIRONMENT; FUNCTION APPROXIMATION; REINFORCEMENT LEARNING AGENT; RELATIONAL LEARNING; RELATIONAL REINFORCEMENT LEARNING; RELATIONAL REPRESENTATIONS; STOCHASTIC SAMPLING; TEMPORAL SAMPLING;

APPROXIMATION ALGORITHMS; ARTIFICIAL INTELLIGENCE; REINFORCEMENT LEARNING;

ITERATIVE METHODS;

EID: 79952415359     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. D. Chapman and L. P. Kaelbling. Input generalization in delayed reinforcement learning: An algorithm and performance comparisons. In Proceedings of IJCAI-91, 1991.
2. K. Driessens, J. Ramon, and H. Blockeel. Speeding up relational reinfocement learning through the use of an incremental first order decision tree learner. In Proceedings of ECML - European Conference on Machine Learning, pages 97-108, 2001.
3. K. Driessens, J. Ramon, and T. Croonenborghs. Transfer learning for reinforcement learning through goal and policy parameterization. Presented at the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, 2006.
4. K. Driessens. Relational Reinforcement Learning. PhD thesis, Department of Computer Science, K.U. Leuven, 2004.
5. S. Dzeroski, L. De Raedt, and K. Driessens. Relational reinforcement learning. Machine Learning, 43(1/2):5-52, April 2001.
6. S. Finney, N. Gardiol, L. Kaelbling, and T. Oates. The thing that we tried didn't work very well : Deictic representation in reinforcement learning. In Proceedings of the 18th Annual Conference on Uncertainty in Artificial Intelligence, pages 154-161, 2002.
7. D. Jensen and J. Neville. Linkage and autocorrelation cause feature selection bias in relational learning. In Proceedings of the 19th International Conference on Machine Learning, pages 259-266, 2002.
8. L.P. Kaebling, M. L. Littman, and A. R. Cassandra. Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101(1-2):99-134, 1998.
9. L. P. Kaelbling, T. Oates, N. Hernandez, and S. Finney. Learning in worlds with objects. In The AAAI Spring Symposium, 2001.
10. A. K. McCallum. Reinforcement Learning with Selective Perception and Hidden State. PhD thesis, University of Rochester, 1995.
11. A. McGovern, L. Friedland, M. Hay, B. Gallagher, A. Fast, J. Neville, and D. Jensen. Exploiting relational structure to understand publication patterns in high-energy physics. SIGKDD Explorations, 5(2):165-172, December 2003.
12. J. Ramon, T. Francis, and H. Blockeel. Learning a tsume-go heuristic with TILDE. In Proceedings of CG2000, the Second International Conference on Computers and Games, volume 2063 of Lecture Notes in Computer Science, pages 151-169. Springer-Verlag, 2001.
13. L. Sachs. Applied Statistics, A handbook of techniques. Springer, 1982.
14. A Srinivasan. A study of two probabilistic methods for searching large spaces with ILP. Data Mining and Knowledge Discovery, 3(1):95-123, 1999.
15. R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
16. P. Tadepalli, R. Givan, and K. Driessens. Relational reinforcement learning: An overview. In Proceedings of the ICML workshop on Relational Reinforcement Learning, 2004.
17. P. E. Utgoff, N.C. Berkman, and J. A. Clouse. Decision tree induction based on efficient tree restructuring. Machine Learning, 29:5-44, 1997. (Pubitemid 127507172)
18. M. van Otterlo. A survey of reinforcement learning in relational domains. Technical Report TR-CTIT-05-31, CTIT Technical Report Series, ISSN 1381-3625, 2005.
19. T. Wolf. The program gotools and its computer-generated tsume go database. Technical report, School of Mathematical Science, 1996.