The Brain & Neural Networks
Online ISSN : 1883-0455
Print ISSN : 1340-766X
ISSN-L : 1340-766X
Search
Advanced search
OR
Browse
Search
Volume 6, Issue 3
Displaying 1-9 of 9 articles from this issue
Show all abstracts
    • |<
    • <
    • 1
    • >
    • >|
  • Adaptive State Space Formation Method for Reinforcement Learning
    Kazuyuki Samejima, Takashi Omori
    1999 Volume 6 Issue 3 Pages 144-154
    Published: September 05, 1999
    Released on J-STAGE: January 17, 2011
    DOIhttps://doi.org/10.3902/jnns.6.144
    JOURNAL FREE ACCESS
    Show abstractHide abstract
    For the application of reinforcement learning to real-world problems, an internal state space has to be constructed from a high dimensional observation space. The algorithm presented here constructs the internal state space during the course of learning desirable actions, and assigns local basis functions adaptively depending on the task requirement. The internal state space initially has only one basis function over the entire observation space, and that basis is eventually divided into smaller ones due to the statistical property of locally weighted temporal difference error. The algorithm was applied to an autonomous robot collision avoidance problem, and the validity of the algorithm was evaluated to show, for instance, the need of a smaller number of basis functions in comparison to other method.
    View full abstract
    Download PDF (1059K)
    • |<
    • <
    • 1
    • >
    • >|
feedback
 Top