NDLI: Potential based policy gradient optimization algorithm for a class of stochastic nonlinear systems

Content Provider	IEEE Xplore Digital Library
Author	Cheng Kang Zhang Kanjian Fei Shumin Wei Haikun
Copyright Year	2015
Description	Author affiliation: Sch. of Autom., Southeast Univ., Nanjing, China (Cheng Kang; Zhang Kanjian; Fei Shumin; Wei Haikun)
Abstract	In this paper, a potential based policy gradient algorithm is presented for infinite horizon optimal control of a class of stochastic nonlinear systems, which is with continuous state space and unknown stochastic noise. First, it is shown that the optimal control problem can be transformed into a Markov decision process. Then, the potential based performance derivative formula is developed for continuous state space. For estimating potential function and state transition density function, RBF neural network and $k_{n}-Nearest$ Neighbor technique are applied. Thus, the system performance gradient with respect to the control parameters can be estimated from a sample path. Finally, the simulation shows the effectiveness of the algorithm.
Starting Page	2496
Ending Page	2500
File Size	351370
Page Count	5
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7260023
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Policy gradient Markov decision processes Neural networks Noise Optimal control kn-nearest neighbor Markov processes optimal control Approximation methods performance potential Nonlinear systems Optimization
Content Type	Text
Resource Type	Article

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