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A new learning algorithm for the hierarchical structure learning automata operating in the nonstationary S-model random environment

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2 Author(s)
N. Baba ; Dept. of Inf. Sci., Osaka Kyoiku Univ., Kashiwara, Japan ; Y. Mogami

An extended algorithm of the relative reward strength algorithm is proposed. It is shown that the proposed algorithm ensures the convergence with probability I to the optimal path under the certain type of nonstationary environment. Several computer simulation results confirm the effectiveness of the proposed algorithm.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:32 ,  Issue: 6 )