Multicriteria reinforcement learning based on a Russian doll method for network routing

The routing in communication networks is typically a multicriteria decision making (MCDM) problem. However, setting the parameters of most used MCDM methods to fit the preferences of a decision maker is often a difficult task. A Russian doll method able to choose the best multicriteria solution according to a context defined beforehand is proposed. This context is given by a set of nested boxes in the criteria space, the shapes of which can be established from objective facts such as technical standards, technical specifications, etc. This kind of method is well suited for self-adaptive systems because it is designed to be able to give pertinent results without interaction with a decision maker, whatever the Pareto front. The Russian doll multicriteria decision method is used with a reinforcement learning to optimize the routing in a mobile ad-hoc network. The results on a case study show that the routing can be finely controlled because of the possibility to include as much parameters as desired to adjust the search of the best solution on Pareto fronts a priori unknown. These results are clearly better than those obtained with the optimization of a weighted sum or the minimization of a Chebyshev distance to a reference point.