The shortest path problem on stochastic graphs is addressed. A stochastic optimal control problem is stated, for which dynamic programming can be used. The complexity of the problem leads us to look for a suboptimal solution making use of neural networks to approximate the cost-to-go function. By introducing the concept of "frontier", an alternative technique is given, for which any feasible policy leads to the destination node. Moreover by using a suitable algorithm, any approximation of the can be used to obtain a proper policy. Barren's results suggest the method might not incur the "curse of dimensionality".

Shortest path problems on stochastic graphs: a neuro dynamic programming approach / M. Baglietto; G. Battistelli; F. Vitali; R. Zoppoli. - STAMPA. - 6:(2003), pp. 6187-6193. (Intervento presentato al convegno 42nd IEEE Conference on Decision and Control tenutosi a Maui, USA) [10.1109/CDC.2003.1272268].

Shortest path problems on stochastic graphs: a neuro dynamic programming approach

BATTISTELLI, GIORGIO;
2003

Abstract

The shortest path problem on stochastic graphs is addressed. A stochastic optimal control problem is stated, for which dynamic programming can be used. The complexity of the problem leads us to look for a suboptimal solution making use of neural networks to approximate the cost-to-go function. By introducing the concept of "frontier", an alternative technique is given, for which any feasible policy leads to the destination node. Moreover by using a suitable algorithm, any approximation of the can be used to obtain a proper policy. Barren's results suggest the method might not incur the "curse of dimensionality".
2003
Proceedings 42nd IEEE Conference on Decision and Control
42nd IEEE Conference on Decision and Control
Maui, USA
M. Baglietto; G. Battistelli; F. Vitali; R. Zoppoli
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/779231
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