Reinforcement learning of goal-directed obstacle-avoiding reaction strategies in an autonomous mobile robot

摘要

In this paper we argue for building reactive autonomous mobile robots through reinforcement connectionist learning. Nevertheless, basic reinforcement learning is a slow process. This paper describes an architecture which deals with complex— high-dimensional and/or continuous—situation and action spaces effectively. This architecture is based on two main ideas. The first is to organize the reactive component into a set of modules in such a way that, roughly, each one of them codifies the prototypical action for a given cluster of situations. The second idea is to use a particular kind of planning for figuring out what part of the action space deserves attention for each cluster of situations. Salient features of the planning process are that it is grounded and that it is invoked only when the reactive component does not generalize correctly its previous experience to the new situation. We also report our experience in solving a basic task that most autonomous mobile robots must face, namely path finding.