The paper is devoted to the application of a machine learning model with reinforcement for automating the planning of the deployment of logging sites in forestry. A method for optimizing the selection of cutting areas based on the algorithm of optimization of the Proximal Policy Optimization is proposed. An information system adapted for processing forest management data in a matrix form and working with geographic information systems has been developed. The experiments conducted demonstrate the ability to find rational options for the placement of cutting areas using the proposed method. The results obtained are promising for the use of intelligent systems in the forestry industry.

Keywords: reinforcement learning, deep learning, cutting areas location, forestry, artificial intelligence, planning optimization, clear-cutting

One of the main socioeconomic issue in the Rostov region is the development of residential complex building. Most of the Russian inhabitants' interests are affected by the residential complex building and this building is one of the most important sectors of economic activity in our country.

Keywords: complex building, building, Rostov region, analysis

Rostov-on-Don is one of the largest cities in Russia, it is planned to renovate the territory of the old airport, which in turn can stimulate an increase in the load on the city's transport system. In this regard, the issue of proportional development of the city's transport infrastructure is becoming especially relevant. The solution to the problem can be the modernization of the current and the creation of a conceptually new transport and logistics structure aimed at using passenger transport. This issue is considered within the framework of the concept of sustainable development of the city.

Keywords: transport infrastructure, renovation, the old airport of Rostov-on-Don, urban passenger transport, sustainable development