2005年
Adaptive organization of generalized behavioral concepts for autonomous robots: Schema-based modular reinforcement learning
2005 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Proceedings
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- 開始ページ
- 601
- 終了ページ
- 606
- 記述言語
- 英語
- 掲載種別
- 出版者・発行元
- IEEE
In this paper, we introduce a reinforcement learning method for autonomous robots to obtain generalized behavioral concepts. Reinforcement learning is a well formulated method for autonomous robots to obtain a new behavioral concept by themselves. However, these behavioral concepts cannot be applied to other environments that are different from the place where the robots have learned the concepts. On the contrary, we, human beings, can apply our behavioral concepts to some different environments, objects, and/or situations. We think this ability owes to some memory structure like Schema System that was originally proposed by J.Piaget. We previously proposed a modular-learning method called Dual-Schemata model. In this paper we add a reinforcement learning mechanism to this model. By being provided with this structure, autonomous robots become able to obtain new generalized behavioral concepts by themselves. We also show this kind of structure will enable autonomous robots to behave appropriately even in a novel socially interactive environment.
- リンク情報
- ID情報
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- Web of Science ID : WOS:000231588200099