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Using strongly connected components as a basis for autonomous skill acquisition in reinforcement learning

Kazemitabar, J ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-01507-6_89
  3. Publisher: 2009
  4. Abstract:
  5. Hierarchical reinforcement learning (HRL) has had a vast range of applications in recent years. Preparing mechanisms for autonomous acquisition of skills has been a main topic of research in this area. While different methods have been proposed to achieve this goal, 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, a linear time algorithm is proposed to find subgoal states of the environment in early episodes of learning. Having subgoals available in early phases of a learning task, results in building skills that dramatically increase the convergence rate of the learning process. © 2009 Springer Berlin Heidelberg
  6. Keywords:
  7. Convergence rates ; Hierarchical reinforcement learning ; In-buildings ; Learning process ; Learning tasks ; Linear-time algorithms ; Skill acquisition ; Strongly connected component ; Strongly connected components ; Subgoals ; Time complexity ; Education ; Mergers and acquisitions ; Neural networks ; Reinforcement ; Reinforcement learning ; Learning algorithms
  8. Source: 6th International Symposium on Neural Networks, ISNN 2009, Wuhan, 26 May 2009 through 29 May 2009 ; Volume 5551 LNCS, Issue PART 1 , 2009 , Pages 794-803 ; 03029743 (ISSN); 3642015069 (ISBN); 9783642015069 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-642-01507-6_89