Reinforcement Machine Learning Model for Sports Infrastructure Development Planning

This paper considers the actual task of planning the rational development of sports infrastructure with limited resources. The development of a mathematical model for the evaluation of sports infrastructure projects and the schedule for their implementation are carried out. To evaluate projects, it is proposed to use methods of multicriteria decision analysis based on fuzzy preference areas. It is difficult to search for the optimal parameters of the proposed model due to the presence of binary variables that make the problem NP-hard. To find a solution close to the optimal one, a machine learning model with reinforcement is proposed. Software is developed that allows both ranking projects and determining the schedule for their implementation, taking into account the available resources and needs. The algorithmic and software solution based on a machine learning model with reinforcement is invariant with respect to the subject area and can also be used in other combinatorial optimization problems. Computational experiments are carried out for the proposed solution on the example of the problem of choosing regions for the construction of basketball courts.