A new learning algorithm for the hierarchical structure learning automata operating in the general multiteacher environment
Learning behaviors of hierarchically structured stochastic automata operating in a general nonstationary multiteacher environment are considered. It is shown that convergence with probability I to the optimal path is ensured by a new learning algorithm which is an extended form of the relative reward strength algorithm. Several computer simulation results confirm the effectiveness of the proposed algorithm.
hierarchical structure learning automata multiteacher environment relative reward strength algorithm