Mutual Learning: Part I - Learning Automata

Learning theory has been studied for a long time by philosophers, and in the last century by psychologists and engineers. Yet, all learning is carried out in a general deterministic or stochastic environment, mostly by one isolated learner. This paper discusses the concept of mutual learning, where two or more entities attempt to learn from each other. The question posed is: “If two or more entities are learning in the same or similar environments trying to solve the same or similar tasks, how can they share their learning to improve themselves?” The authors believe that this is a central question that will keep researchers busy for many years. The paper merely introduces this question for discussion, and suggests some preliminary answers using the well known stochastic learning automaton framework for reinforcement learning.