Pittsburgh learning classifier systems for explainable reinforcement learning-Reference-Cited by-同舟云学术

Pittsburgh learning classifier systems for explainable reinforcement learning

Published:2022-07-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Bishop Jordan T.¹,Gallagher Marcus¹,Browne Will N.²

Affiliation:

1. The University of Queensland, Australia

2. Queensland University of Technology, Australia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3512290.3528767

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3. Jaume Bacardit and Josep Maria Garrell . 2007. Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System . In Learning Classifier Systems (Lecture Notes in Computer Science), Tim Kovacs, Xavier Llorà, Keiki Takadama, Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W . Wilson (Eds.). Springer , Berlin, Heidelberg , 59--79. Jaume Bacardit and Josep Maria Garrell. 2007. Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System. In Learning Classifier Systems (Lecture Notes in Computer Science), Tim Kovacs, Xavier Llorà, Keiki Takadama, Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W. Wilson (Eds.). Springer, Berlin, Heidelberg, 59--79.

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