A new learning algorithm for the MAXQ hierarchical reinforcement learning method

Mirzazadeh, F ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/ICICT.2007.375352
  3. Publisher: 2007
  4. Abstract:
  5. The MAXQ hierarchical reinforcement learning method is computationally expensive in applications with deep hierarchy. In this paper, we propose a new learning algorithm for MAXQ method to address the open problem of reducing its computational complexity. While the computational cost of the algorithm is considerably decreased, the required storage of new algorithm is less than two times as the original learning algorithm requires storage. Our experimental results in the simple Taxi Domain Problem show satisfactory behavior of the new algorithm
  6. Keywords:
  7. Computational complexity ; Hierarchical systems ; Learning systems ; Problem solving ; Open problems ; Taxi Domain Problems ; Learning algorithms
  8. Source: ICICT 2007: International Conference on Information and Communication Technology, Dhaka, 7 March 2007 through 9 March 2007 ; 2007 , Pages 105-108 ; 9843233948 (ISBN); 9789843233943 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4261375