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Automatic discovery of subgoals in reinforcement learning using strongly connected components

Kazemitabar, J ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-02490-0_101
  3. Publisher: 2009
  4. Abstract:
  5. The hierarchical structure of real-world problems has resulted in a focus on hierarchical frameworks in the reinforcement learning paradigm. Preparing mechanisms for automatic discovery of macro-actions has mainly concentrated on subgoal discovery methods. Among the proposed algorithms, those based on graph partitioning have achieved precise results. However, few methods have been shown to be successful both in performance and also efficiency in terms of time complexity of the algorithm. In this paper, we present a SCC-based subgoal discovery algorithm; a graph theoretic approach for automatic detection of subgoals in linear time. Meanwhile a parameter tuning method is proposed to find the only parameter of the method. © 2009 Springer Berlin Heidelberg
  6. Keywords:
  7. Automatic detection ; Automatic discovery ; Discovery algorithm ; Graph partitioning ; Graph theoretic approach ; Hierarchical structures ; Linear time ; Parameter-tuning ; Real-world problem ; Strongly connected component ; Subgoals ; Time complexity ; Algorithms ; Data processing ; Reinforcement learning ; Translation (languages) ; Reinforcement
  8. Source: 15th International Conference on Neuro-Information Processing, ICONIP 2008, Auckland, 25 November 2008 through 28 November 2008 ; Volume 5506 LNCS, Issue PART 1 , 2009 , Pages 829-834 ; 03029743 (ISSN); 3642024890 (ISBN); 9783642024894 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-642-02490-0_101