Speeding up Relational Reinforcement Learning Through the Use of an Incremental First Order Decision Tree Learner

Speeding up Relational Reinforcement Learning Through the Use of an Incremental First Order Decision Tree Learner (2001)

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by Kurt Driessens , Jan Ramon , Hendrik Blockeel

Venue:	Proceedings of the 13th European Conference on Machine Learning
Citations:	32 - 18 self

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@INPROCEEDINGS{Driessens01speedingup,
    author = {Kurt Driessens and Jan Ramon and Hendrik Blockeel},
    title = {Speeding up Relational Reinforcement Learning Through the Use of an Incremental First Order Decision Tree Learner},
    booktitle = {Proceedings of the 13th European Conference on Machine Learning},
    year = {2001},
    pages = {97--108},
    publisher = {Springer-Verlag}
}

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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.

Citations

2827	Reinforcement Learning: An Introduction - Sutton, Barto - 1998
1134	Reinforcement learning: a survey - Kaelbling, Littman, et al. - 1996
117	input generalization in delayed reinforcement learning: an algorithm and performance comparisons - Chapman, Kaelbling
92	Bayesian logic programs - Kersting, Raedt - 2001
88	Relational reinforcement learning - Dzeroski, Raedt, et al.
83	Top-down induction of clustering trees - Blockeel, Raedt, et al. - 1998
18	Raedt. Top-down induction of order logical decision trees - Blockeel, De - 1998
17	Executing query packs in ILP - Blockeel, Dehaspe, et al. - 2000