Adaptive stochastic cellular automata: Applications

S. Qian; Y. C. Lee; R. D. Jones; C. W. Barnes; G. W. Flake; M. K. O'Rourke; K. Lee; H. H. Chen; G. Z. Sun; Y. Q. Zhang; D. Chen; C. L. Giles

doi:10.1016/0167-2789(90)90181-N

Adaptive stochastic cellular automata: Applications

S. Qian, Y. C. Lee, R. D. Jones, C. W. Barnes, G. W. Flake, M. K. O'Rourke, K. Lee, H. H. Chen, G. Z. Sun, Y. Q. Zhang, D. Chen, C. L. Giles

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

The stochastic learning cellular automata model has been applied to the problem of controlling unstable systems. Two example unstable systems studied are controlled by an adaptive stochastic cellular automata algorithm with an adaptive critic. The reinforcement learning algorithm and the architecture of the stochastic CA controller are presented. Learning to balance a single pole is discussed in detail. Balancing an inverted double pendulum highlights the power of the stochastic CA approach. The stochastic CA model is compared to conventional adaptive control and artificial neural network approaches.

Original language	English (US)
Pages (from-to)	181-188
Number of pages	8
Journal	Physica D: Nonlinear Phenomena
Volume	45
Issue number	1-3
DOIs	https://doi.org/10.1016/0167-2789(90)90181-N
State	Published - Sep 2 1990

All Science Journal Classification (ASJC) codes

Statistical and Nonlinear Physics
Mathematical Physics
Condensed Matter Physics
Applied Mathematics

Access to Document

10.1016/0167-2789(90)90181-N

Cite this

@article{f7cecc94aa6143098e4281e78ac459c3,

title = "Adaptive stochastic cellular automata: Applications",

abstract = "The stochastic learning cellular automata model has been applied to the problem of controlling unstable systems. Two example unstable systems studied are controlled by an adaptive stochastic cellular automata algorithm with an adaptive critic. The reinforcement learning algorithm and the architecture of the stochastic CA controller are presented. Learning to balance a single pole is discussed in detail. Balancing an inverted double pendulum highlights the power of the stochastic CA approach. The stochastic CA model is compared to conventional adaptive control and artificial neural network approaches.",

author = "S. Qian and Lee, {Y. C.} and Jones, {R. D.} and Barnes, {C. W.} and Flake, {G. W.} and O'Rourke, {M. K.} and K. Lee and Chen, {H. H.} and Sun, {G. Z.} and Zhang, {Y. Q.} and D. Chen and Giles, {C. L.}",

year = "1990",

month = sep,

day = "2",

doi = "10.1016/0167-2789(90)90181-N",

language = "English (US)",

volume = "45",

pages = "181--188",

journal = "Physica D: Nonlinear Phenomena",

issn = "0167-2789",

publisher = "Elsevier B.V.",

number = "1-3",

}

TY - JOUR

T1 - Adaptive stochastic cellular automata

T2 - Applications

AU - Qian, S.

AU - Lee, Y. C.

AU - Jones, R. D.

AU - Barnes, C. W.

AU - Flake, G. W.

AU - O'Rourke, M. K.

AU - Lee, K.

AU - Chen, H. H.

AU - Sun, G. Z.

AU - Zhang, Y. Q.

AU - Chen, D.

AU - Giles, C. L.

PY - 1990/9/2

Y1 - 1990/9/2

N2 - The stochastic learning cellular automata model has been applied to the problem of controlling unstable systems. Two example unstable systems studied are controlled by an adaptive stochastic cellular automata algorithm with an adaptive critic. The reinforcement learning algorithm and the architecture of the stochastic CA controller are presented. Learning to balance a single pole is discussed in detail. Balancing an inverted double pendulum highlights the power of the stochastic CA approach. The stochastic CA model is compared to conventional adaptive control and artificial neural network approaches.

AB - The stochastic learning cellular automata model has been applied to the problem of controlling unstable systems. Two example unstable systems studied are controlled by an adaptive stochastic cellular automata algorithm with an adaptive critic. The reinforcement learning algorithm and the architecture of the stochastic CA controller are presented. Learning to balance a single pole is discussed in detail. Balancing an inverted double pendulum highlights the power of the stochastic CA approach. The stochastic CA model is compared to conventional adaptive control and artificial neural network approaches.

UR - http://www.scopus.com/inward/record.url?scp=0348174661&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0348174661&partnerID=8YFLogxK

U2 - 10.1016/0167-2789(90)90181-N

DO - 10.1016/0167-2789(90)90181-N

M3 - Article

AN - SCOPUS:0348174661

SN - 0167-2789

VL - 45

SP - 181

EP - 188

JO - Physica D: Nonlinear Phenomena

JF - Physica D: Nonlinear Phenomena

IS - 1-3

ER -

Adaptive stochastic cellular automata: Applications

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this