### Abstract

The development and evaluation of two novel nonlinear reinforcement schemes for learning automata are presented. These schemes are designed to increase the rate of adaptation of the existing L_{R-P} schemes while interacting with nonstationary environments. The first of these two schemes is called a nonlinear scheme incorporating history (NSIH) and the second a nonlinear scheme with unstable zones (NSWUZ). The prime objective of these algorithms is to reduce the number of iterations needed for the action probability vector to reach the desired level of accuracy rather than converge to a specific unit vector in the Cartesian coordinate. Simulation experiments have been conducted to assess the learning properties of NSIH and NSWUZ in nonstationary environments. The simulation results show that the proposed nonlinear algorithms respond to environmental changes faster than the L_{R-P} scheme.

Original language | English (US) |
---|---|

Pages (from-to) | 2204-2207 |

Number of pages | 4 |

Journal | Proceedings of the IEEE Conference on Decision and Control |

Volume | 4 |

State | Published - 1990 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Chemical Health and Safety
- Control and Systems Engineering
- Safety, Risk, Reliability and Quality

### Cite this

*Proceedings of the IEEE Conference on Decision and Control*,

*4*, 2204-2207.

}

*Proceedings of the IEEE Conference on Decision and Control*, vol. 4, pp. 2204-2207.

**Nonlinear reinforcement schemes for learning automata.** / Garcia, Humberto E.; Ray, Asok.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Nonlinear reinforcement schemes for learning automata

AU - Garcia, Humberto E.

AU - Ray, Asok

PY - 1990

Y1 - 1990

N2 - The development and evaluation of two novel nonlinear reinforcement schemes for learning automata are presented. These schemes are designed to increase the rate of adaptation of the existing LR-P schemes while interacting with nonstationary environments. The first of these two schemes is called a nonlinear scheme incorporating history (NSIH) and the second a nonlinear scheme with unstable zones (NSWUZ). The prime objective of these algorithms is to reduce the number of iterations needed for the action probability vector to reach the desired level of accuracy rather than converge to a specific unit vector in the Cartesian coordinate. Simulation experiments have been conducted to assess the learning properties of NSIH and NSWUZ in nonstationary environments. The simulation results show that the proposed nonlinear algorithms respond to environmental changes faster than the LR-P scheme.

AB - The development and evaluation of two novel nonlinear reinforcement schemes for learning automata are presented. These schemes are designed to increase the rate of adaptation of the existing LR-P schemes while interacting with nonstationary environments. The first of these two schemes is called a nonlinear scheme incorporating history (NSIH) and the second a nonlinear scheme with unstable zones (NSWUZ). The prime objective of these algorithms is to reduce the number of iterations needed for the action probability vector to reach the desired level of accuracy rather than converge to a specific unit vector in the Cartesian coordinate. Simulation experiments have been conducted to assess the learning properties of NSIH and NSWUZ in nonstationary environments. The simulation results show that the proposed nonlinear algorithms respond to environmental changes faster than the LR-P scheme.

UR - http://www.scopus.com/inward/record.url?scp=0025561605&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0025561605&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0025561605

VL - 4

SP - 2204

EP - 2207

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

SN - 0191-2216

ER -