Enhancing Autonomous Agents Evolution with Learning by Imitation

This paper presents a new mechanism to enhance the evolutionary process of autonomous agents through lifetime adaptation by imitation. Imitation is an effective method for learning new traits and is naturally applicable within the evolutionary paradigm. We describe a set of simulations where a population of agents evolve to solve a certain task. In each generation, individuals can select other agents from the population as models (teachers) and imitate their behavior. In contradistinction to previous studies, we focus on the interaction between imitation and evolution when imitation takes place only across members of the same generation, and does not percolate across generations via vertical (cultural) transmission. We show how this mechanism can be applied to successfully enhance the evolution of autonomous agents, when
other forms of learning are not applicable.