Towards Real Learning Robots

By (author) Hailu, Getachew

Reinforcement learning, in a nutshell, is a form of learning that enables the robot to construct a control law by a system of feedback signals that reinforce electrical path ways that produce correct response, and conversely wipe-out connections that produce errors. Unfortunately, without biasing, it is a weak learning that presents unreasonable difficulty, especially when it is applied to real robots. The subject of this thesis is to study, for a particular class of problems, the effects of different form of biases on the speed of learning as well as on the quality of final learned policy, and to realize this learning paradigm on a physical robot by appropriately biasing the robot with domain knowledge that determines how much the robot knows about the different parts of its world.

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  • Contents: Kalman Filter - Reinforcement Learning - Bias - Belief Matrix.

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書名 Towards Real Learning Robots
著作者等 Hailu, Getachew
シリーズ名 European University Studies, Series 41: Computer Science v. 33
出版元 Peter Lang GmbH
刊行年月 2000.01.01
ページ数 175p
大きさ H210 x W148
ISBN 9783631359600
言語 英語
出版国 ドイツ