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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルGECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
出版社Association for Computing Machinery
ページ81-82
ページ数2
ISBN(印刷版)9781450328814
DOI
出版ステータス出版済み - 1 1 2014
イベント16th Genetic and Evolutionary Computation Conference, GECCO 2014 - Vancouver, BC, カナダ
継続期間: 7 12 20147 16 2014

出版物シリーズ

名前GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference

その他

その他16th Genetic and Evolutionary Computation Conference, GECCO 2014
Countryカナダ
CityVancouver, BC
Period7/12/147/16/14

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Applied Mathematics

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