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Fast reinforcement learning for simple physical robots
http://hdl.handle.net/10445/5286
http://hdl.handle.net/10445/5286d0ada507-81cf-49e7-a848-99cd90b3a762
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2010-11-29 | |||||
| タイトル | ||||||
| タイトル | Fast reinforcement learning for simple physical robots | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Hartono, Pitoyo
× Hartono, Pitoyo× Kakita, Sachiko |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In the past few years, the field of autonomous robot has been rigorously studied and non-industrial applications of robotics are rapidly emerging. One of the most interesting aspects of this field is the development of the learning ability which enables robots to autonomously adapt to given environments without human guidance. As opposed to the conventional methods of robots' control, where human logically design the behavior of a robot, the ability to acquire action strategies through some learning processes will not only significantly reduce the production costs of robots but also improves the applicability of robots in wider tasks and environments. However, learning algorithms usually require large calculation cost, which make them unsuitable for robots with limited resources. In this study, we propose a simple two-layered neural network that implements a novel and fast Reinforcement Learning. The proposed learning method requires significantly less calculation resources, hence applicable to small physical robots running in the real world environments.For this study, we built several simple robots and implemented the proposed learning mechanism to them. In the experiments, to evaluate the efficacy of the proposed learning mechanism, several robots were simultaneously trained to acquire obstacle avoidance strategies in a same environment, thus, forming a dynamic environment where the learning task is substantially harder than in the case of learning in a static environment. | |||||
| 書誌情報 |
Memetic Computing Journal 巻 1, 号 4, p. 305-313, 発行日 2009 |
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| 査読有無 | ||||||
| 値 | あり/yes | |||||
| 研究業績種別 | ||||||
| 値 | 原著論文/Original Paper | |||||
| 単著共著 | ||||||
| 値 | 共著/joint | |||||
| 出版者 | ||||||
| 出版者 | Springer | |||||
