ID | 33049 |
フルテキストURL | |
著者 |
Ito, Kazuyuki
Okayama University
Matsuno, Fumitoshi
Tokyo Institute of Technology
|
抄録 | Reinforcement learning is very effective for robot learning. It is because it does not need prior knowledge and has higher capability of reactive and adaptive behaviors. In our previous works, we proposed new reinforce learning algorithm: "Q-learning with dynamic structuring of exploration space based on genetic algorithm (QDSEGA)". It is designed for complicated systems with large action-state space like a robot with many redundant degrees of freedom. However the application of QDSEGA is restricted to static systems. A snake-like robot has many redundant degrees of freedom and the dynamics of the system are very important to complete the locomotion task. So application of usual reinforcement learning is very difficult. In this paper, we extend layered structure of QDSEGA so that it becomes possible to apply it to real robots that have complexities and dynamics. We apply it to acquisition of locomotion pattern of the snake-like robot and demonstrate the effectiveness and the validity of QDSEGA with the extended layered structure by simulation and experiment. |
キーワード | genetic algorithms
learning (artificial intelligence)
mobile robots
motion control
robot dynamics
robot kinematics
|
備考 | Published with permission from the copyright holder. this is the institute's copy, as published in Robotics and Automation, 2003. Proceedings. ICRA '03. IEEE International Conference on
, 14-19 Sept. 2003, Volume 1, Pages 791-796.
Publisher URL:http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1241690 Copyright © 2003 IEEE. All rights reserved. |
発行日 | 2003-09-14
|
出版物タイトル |
Robotics and Automation
|
巻 | 1巻
|
開始ページ | 791
|
終了ページ | 796
|
資料タイプ |
学術雑誌論文
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言語 |
英語
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査読 |
有り
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DOI | |
Submission Path | mechanical_engineering/7
|