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Learning behaviours of variable-structure stochastic automata under a multiteacher environment are considered. The concepts of absolute expediency and ε-optimality in a single-teacher environment are extended by the introduction of an average weighted reward and are redefined for a multiteacher environment. As an extended form of the absolutely expedient learning algorithm, a general class of nonlinear learning algorithm, called the GAE scheme, is proposed as a reinforcement scheme in a multiteacher environment. It is shown that the GAE scheme is absolutely expedient and ε-optimal in the general n-teacher environment. Learning behaviours of the GAE scheme in various multiteacher environments are simulated by computer and the results indicate the effectiveness of the GAE scheme.