抄録
For hierarchical structure learning automata operating in a nonstationary random environment, in this paper, a new learning algorithm is constructed by extending the relative reward strength algorithm proposed by Simha and Kurose. The learning propertiy of our algorithm is considered theoretically, and it is proved that the path probability of the optimal path can be approached 1 as much as possible by using our algorithm. In numerical simulation, the number of iterations of our algorithm is compared with that of the hierarchical structure learning algorithm proposed by Thathachar and Ramakrishnan, and it is shown that our algorithm can find the optimal path after the smaller number of iterations than that of the algorithm of Thathachar and Ramakrishnan.
