Novelty-Organizing Classifiers applied to classification and reinforcement learning: Towards flexible algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

It is widely known that reinforcement learning is a more general problem than supervised learning. In fact, supervised learning can be seen as a class of reinforcement learning problems. However, only a couple of papers tested reinforcement learning algorithms in supervised learning problems. Here we propose a new and simpler way to abstract supervised learning for any reinforcement learning algorithm. Moreover, a new algorithm called Novelty-Organizing Classifiers is developed based on a Novelty Map population that focuses more on the novelty of the inputs than their frequency. A comparison of the proposed method with Self-Organizing Classifiers and BioHel on some datasets is presented. Even though BioHel is specialized in solving supervised learning problems, the results showed only a trade-off between the algorithms. Lastly, results on a maze problem validate the flexibility of the proposed algorithm beyond supervised learning problems. Thus, Novelty-Organizing Classifiers is capable of solving many supervised learning problems as well as a maze problem without changing any parameter at all. Considering the fact that no adaptation of parameters was executed, the proposed algorithm's basis seems interestingly flexible.

Original languageEnglish
Title of host publicationGECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Pages81-82
Number of pages2
ISBN (Print)9781450328814
DOIs
Publication statusPublished - 2014
Event16th Genetic and Evolutionary Computation Conference, GECCO 2014 - Vancouver, BC, Canada
Duration: Jul 12 2014Jul 16 2014

Other

Other16th Genetic and Evolutionary Computation Conference, GECCO 2014
CountryCanada
CityVancouver, BC
Period7/12/147/16/14

Fingerprint

Supervised learning
Reinforcement learning
Reinforcement Learning
Supervised Learning
Classifiers
Classifier
Learning algorithms
Learning Algorithm
Self-organizing
Trade-offs
Flexibility

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Vargas, D. V., Takano, H., & Murata, J. (2014). Novelty-Organizing Classifiers applied to classification and reinforcement learning: Towards flexible algorithms. In GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference (pp. 81-82). Association for Computing Machinery. https://doi.org/10.1145/2598394.2598429

Novelty-Organizing Classifiers applied to classification and reinforcement learning : Towards flexible algorithms. / Vargas, Danilo Vasconcellos; Takano, Hirotaka; Murata, Junichi.

GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, 2014. p. 81-82.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Vargas, DV, Takano, H & Murata, J 2014, Novelty-Organizing Classifiers applied to classification and reinforcement learning: Towards flexible algorithms. in GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, pp. 81-82, 16th Genetic and Evolutionary Computation Conference, GECCO 2014, Vancouver, BC, Canada, 7/12/14. https://doi.org/10.1145/2598394.2598429
Vargas DV, Takano H, Murata J. Novelty-Organizing Classifiers applied to classification and reinforcement learning: Towards flexible algorithms. In GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference. Association for Computing Machinery. 2014. p. 81-82 https://doi.org/10.1145/2598394.2598429
Vargas, Danilo Vasconcellos ; Takano, Hirotaka ; Murata, Junichi. / Novelty-Organizing Classifiers applied to classification and reinforcement learning : Towards flexible algorithms. GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, 2014. pp. 81-82
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