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• W1483193721 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." @default.
• W1483193721 created "2016-06-24" @default.
• W1483193721 creator A5015725151 @default.
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• W1483193721 date "2015-07-01" @default.
• W1483193721 modified "2023-09-23" @default.
• W1483193721 title "Potential based policy gradient optimization algorithm for a class of stochastic nonlinear systems" @default.
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• W1483193721 doi "https://doi.org/10.1109/chicc.2015.7260023" @default.
• W1483193721 hasPublicationYear "2015" @default.
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