Learning behaviors of the hierarchical structure stochastic automata operating in the nonstationary multiteacher environment are considered. A new learning algorithm which extends the idea of the rela- tive reward strength algorithm is proposed. It is shown that the proposed algorithm ensures convergence to the optimal path under a certain type of the nonstationary multiteacher environment. Learning behaviors of the proposed algorithm are simulated by computer and the results indicate its effectiveness.
['Norio Baba', 'Yoshio Mogami']
A Consideration on the Learning Behaviors of the HSLA Under the Nonstationary Multiteacher Environment and Their Application to Simulation and Gaming