A relative reward-strength algorithm for the hierarchical structure learning automata operating in the general nonstationary multiteacher environment
A relative reward-strength algorithm for the hierarchical structure learning automata operating in the general nonstationary multiteacher environment
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DOI：
10.1109/tsmcb.2005.862489
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发表时间：
2006-08
期刊：
影响因子：
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通讯作者：
N. Baba;Y. Mogami
中科院分区：
文献类型：
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作者：
N. Baba;Y. Mogami
提出了一种在非平稳多教师环境（NME）中运行的层次结构学习自动机（HSLA）的新学习算法。所提出的算法是通过扩展在一般 NME 中运行的 HSLA 中使用的原始相对奖励强度算法而导出的。结果表明，在某种类型的 NME 下，该算法保证以概率 1 收敛到最优路径。为了将所提出的算法在一些 NME 中的相对性能与当今最快的两种算法的相对性能进行比较，进行了一些计算机模拟结果，证实了所提出的算法的有效性
A new learning algorithm for the hierarchical structure learning automata (HSLA) operating in the nonstationary multiteacher environment (NME) is proposed. The proposed algorithm is derived by extending the original relative reward-strength algorithm to be utilized in the HSLA operating in the general NME. It is shown that the proposed algorithm ensures convergence with probability 1 to the optimal path under a certain type of the NME. Several computer-simulation results, which have been carried out in order to compare the relative performance of the proposed algorithm in some NMEs against those of the two of the fastest algorithms today, confirm the effectiveness of the proposed algorithm