NDLI: Learning to control a joint driven double inverted pendulum using nested actor/critic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Kobori, N. Suzuki, K. Hartono, P. Hashimoto, S.
Copyright Year	2002
Description	Author affiliation: Dept. of Appl. Phys., Waseda Univ., Tokyo, Japan (Kobori, N.; Suzuki, K.; Hartono, P.; Hashimoto, S.)
Abstract	In recent years, 'Reinforcement Learning' which can acquire reflective and adaptive actions, is becoming the center of attention as a learning method for robotics control. However, there are many unsolved problems that have to be cleared in order to put the method into practical use. One of the problems is the handling of the state space and the action space. Many algorithms of existing reinforcement learning deal with discrete state space and action space. When the unit of search space is rough, a subtle control cannot be achieved (imperfect perception). On the contrary, when the unit of search space is too fine, searching space is enlarged accordingly and the stable convergence of learning cannot be obtained (curse of dimensionality). In this paper, we propose a nested actor/critic algorithm that can deal with the continuous state and action space. The method proposed in this paper inserts a child actor/critic into the actor part of parent actor/critic algorithm. We examined the proposed algorithm for a stable control problem in both simulation and prototype model of a joint-driven double inverted pendulum.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	2610
Ending Page	2614
File Size	456814
Page Count	5
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1201968
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	State-space methods Learning systems Machine learning algorithms Physics Orbital robotics Convergence Machine learning System performance Grid computing Control systems
Content Type	Text
Resource Type	Article

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