NDLI: Continuous valued Q-learning method able to incrementally refine state space

Content Provider	IEEE Xplore Digital Library
Author	Takeda, M. Nakamura, T. Ogasawara, T.
Copyright Year	2001
Description	Author affiliation: Wako Res. Center, Honda R&D Co. Ltd, Saitama, Japan (Takeda, M.)
Abstract	The conventional reinforcement learning method has problems in applying to real robot tasks, because such method must be able to represent the values in terms of infinitely many states and action pairs. In order to represent an action value function continuously, a function approximation method is usually applied. In our previous work (2000), we pointed out that this type of learning method potentially has a discontinuity problem of optimal actions for a given state. In this paper, we propose a method for estimating where a discontinuity of the optimal action takes place and for refining a state space incrementally. We call this method an continuous valued Q-learning method. To show the validity of our method, we apply the method to a simulated robot.
Sponsorship	IEEE Ind. Electron. Soc.
Starting Page	265
Ending Page	271
File Size	739451
Page Count	7
File Format	PDF
ISBN	0780366123
DOI	10.1109/IROS.2001.973369
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-10-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	State-space methods State estimation Orbital robotics Learning systems Quantization Research and development Information science Space technology Robots Statistical analysis
Content Type	Text
Resource Type	Article

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