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Observation and Imitation: Goal Sequence Learning in Neurally Controlled Construction Animats: VI-MA

We report on an under-explored animat learning problem, that of learning sequences of goals, and present an algorithm for solving it using observation and imitation. The presentation introduces the problem as well as a neural agent control architecture we use as a framework in which to use the algorithm. We demonstrate that, by using the algorithm, a learning agent can learn to satisfy a sequence of goals by observing the actions of a teaching agent, and later imitate the teaching agent.