Solving Stochastic Shortest Path Problem Using Monte Carlo Sampling Method: A Distributed Learning Automata Approach

2003 ◽  
pp. 626-631 ◽  
Author(s):  
M. R. Meybodi ◽  
Hamid Beigy
Keyword(s):  
Monte Carlo ◽  
Shortest Path ◽  
Sampling Method ◽  
Learning Automata ◽  
Distributed Learning ◽  
Monte Carlo Sampling ◽  
Shortest Path Problem ◽  
Stochastic Shortest Path ◽  
Distributed Learning Automata ◽  
Stochastic Shortest Path Problem

UTILIZING DISTRIBUTED LEARNING AUTOMATA TO SOLVE STOCHASTIC SHORTEST PATH PROBLEMS

2006 ◽  
Vol 14 (05) ◽  
pp. 591-615 ◽  
Author(s):  
HAMID BEIGY ◽  
M. R. MEYBODI
Keyword(s):  
Shortest Path ◽  
Learning Automata ◽  
Iterative Algorithms ◽  
Distributed Learning ◽  
Destination Node ◽  
Stochastic Shortest Path ◽  
Shortest Path Problems ◽  
Distributed Learning Automata ◽  
Stochastic Shortest Path Problems ◽  
Stochastic Shortest Path Problem

In this paper, we first introduce a network of learning automata, which we call it as distributed learning automata and then propose some iterative algorithms for solving stochastic shortest path problem. These algorithms use distributed learning automata to find a policy that determines a path from a source node to a destination node with minimal expected cost (length). In these algorithms, at each stage distributed learning automata determines which edges to be sampled. This sampling method may result in decreasing unnecessary samples and hence decreasing the running time of algorithms. It is shown that the shortest path is found with a probability as close as to unity by proper choice of the parameters of the proposed algorithms.

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