NDLI: Stochastic real-valued reinforcement learning to solve a nonlinear control problem

Content Provider	IEEE Xplore Digital Library
Author	Kimura, H. Kobayashi, S.
Copyright Year	1999
Description	Author affiliation: Dept. of Comput. Intelligence & Syst. Sci., Tokyo Inst. of Technol., Japan (Kimura, H.)
Abstract	This paper presents a new approach to reinforcement learning (RL) to solve a nonlinear control problem efficiently in which state and action spaces are continuous. We provide a hierarchical RL algorithm composed of local linear controllers and TD-learning, which are both very simple. The continuous state space is discretized into an array of coarse boxes, and each box has its own local linear controller for choosing primitive continuous actions. The higher-level of the hierarchy accumulates state-values using tables with one entry for each box. Each linear controller improves the local control policy by using an actor-critic method. The algorithm was applied to a simulation of a cart-pole swing-up problem, and feasible solutions are found in less time than those of conventional discrete RL methods.
Starting Page	510
Ending Page	515
File Size	554023
Page Count	6
File Format	PDF
ISBN	0780357310
ISSN	1062922X
DOI	10.1109/ICSMC.1999.815604
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-10-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Learning Space technology Computational intelligence Nonlinear control systems Control systems State-space methods Interpolation Bridges Quantization
Content Type	Text
Resource Type	Article

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