The search functionality is under construction.

The search functionality is under construction.

A robust routing algorithm was developed based on reinforcement learning that uses (1) reward-weighted principal component analysis, which compresses the state space of a network with a large number of nodes and eliminates the adverse effects of various types of attacks or disturbance noises, (2) activity-oriented index allocation, which adaptively constructs a basis that is used for approximating routing probabilities, and (3) newly developed space compression based on a potential model that reduces the space for routing probabilities. This algorithm takes all the network states into account and reduces the adverse effects of disturbance noises. The algorithm thus works well, and the frequencies of causing routing loops and falling to a local optimum are reduced even if the routing information is disturbed.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E91-A No.7 pp.1733-1740

- Publication Date
- 2008/07/01

- Publicized

- Online ISSN
- 1745-1337

- DOI
- 10.1093/ietfec/e91-a.7.1733

- Type of Manuscript
- PAPER

- Category
- Nonlinear Problems

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

Copy

Hideki SATOH, "A Nonlinear Approach to Robust Routing Based on Reinforcement Learning with State Space Compression and Adaptive Basis Construction" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 7, pp. 1733-1740, July 2008, doi: 10.1093/ietfec/e91-a.7.1733.

Abstract: A robust routing algorithm was developed based on reinforcement learning that uses (1) reward-weighted principal component analysis, which compresses the state space of a network with a large number of nodes and eliminates the adverse effects of various types of attacks or disturbance noises, (2) activity-oriented index allocation, which adaptively constructs a basis that is used for approximating routing probabilities, and (3) newly developed space compression based on a potential model that reduces the space for routing probabilities. This algorithm takes all the network states into account and reduces the adverse effects of disturbance noises. The algorithm thus works well, and the frequencies of causing routing loops and falling to a local optimum are reduced even if the routing information is disturbed.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.7.1733/_p

Copy

@ARTICLE{e91-a_7_1733,

author={Hideki SATOH, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={A Nonlinear Approach to Robust Routing Based on Reinforcement Learning with State Space Compression and Adaptive Basis Construction},

year={2008},

volume={E91-A},

number={7},

pages={1733-1740},

abstract={A robust routing algorithm was developed based on reinforcement learning that uses (1) reward-weighted principal component analysis, which compresses the state space of a network with a large number of nodes and eliminates the adverse effects of various types of attacks or disturbance noises, (2) activity-oriented index allocation, which adaptively constructs a basis that is used for approximating routing probabilities, and (3) newly developed space compression based on a potential model that reduces the space for routing probabilities. This algorithm takes all the network states into account and reduces the adverse effects of disturbance noises. The algorithm thus works well, and the frequencies of causing routing loops and falling to a local optimum are reduced even if the routing information is disturbed.},

keywords={},

doi={10.1093/ietfec/e91-a.7.1733},

ISSN={1745-1337},

month={July},}

Copy

TY - JOUR

TI - A Nonlinear Approach to Robust Routing Based on Reinforcement Learning with State Space Compression and Adaptive Basis Construction

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 1733

EP - 1740

AU - Hideki SATOH

PY - 2008

DO - 10.1093/ietfec/e91-a.7.1733

JO - IEICE TRANSACTIONS on Fundamentals

SN - 1745-1337

VL - E91-A

IS - 7

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - July 2008

AB - A robust routing algorithm was developed based on reinforcement learning that uses (1) reward-weighted principal component analysis, which compresses the state space of a network with a large number of nodes and eliminates the adverse effects of various types of attacks or disturbance noises, (2) activity-oriented index allocation, which adaptively constructs a basis that is used for approximating routing probabilities, and (3) newly developed space compression based on a potential model that reduces the space for routing probabilities. This algorithm takes all the network states into account and reduces the adverse effects of disturbance noises. The algorithm thus works well, and the frequencies of causing routing loops and falling to a local optimum are reduced even if the routing information is disturbed.

ER -