2-310 A Consideration on the Learning Behaviors of the Hierarchical Structure Learning Automata Operating in the Nonstationary S-model Random Environment

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概要

• 論文の詳細を見る

• Learning behaviors of hierarchical structure learning automata (HSLA) operating in the nonstationary S-model environment are considered. It is shown that an extended algorithm of relative reward strength algorithm ensure convergence to the optimal path with probability 1. Several computer simulation results confirm the effectiveness of the proposed algorithm. Further, learning behaviors of HSLA under the nonstationary multiteacher environment are also considered.

• 一般社団法人日本機械学会の論文
• 2002-11-14

著者

• Baba N

  Dep. Of Information Science Osaka Kyoiku Univ.

• Mogami Y

  Univ. Tokushima Tokushima‐shi Jpn

• Baba Norio

  Dep. of Information Science, Osaka Kyoiku Univ.

• Mogami Yoshio

  Dep. of Information Sci. and Intelligent Sys., Fac. of Engineering, Univ. of Tokushima

関連論文

• 2-310 A Consideration on the Learning Behaviors of the Hierarchical Structure Learning Automata Operating in the Nonstationary S-model Random Environment

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