title: ÐÐ½ÐµÐ´ÑÐµÐ½Ð¸Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼ Ð´Ð»Ñ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÐµÑÑÐ¾Ð½Ð°Ð»Ð° Ð² Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ ÑÑÐµÐ´Ð°Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Implementation of reinforcement learning to ensure personnel safety in automated environments
Creators: name: ÐÐµÐ»ÐµÐ·Ð½ÑÐº, ÐÑÑÑÐ¼
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐ²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²Ð°
Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÑ Ð¿ÐµÑÑÐ¾Ð½Ð°Ð»Ð°
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼
personnel safety
reinforcement learning

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description: Theme of the master's thesis: Â«Implementation of reinforcement learning to ensure personnel safety in automated environmentsÂ». The purpose of the research is development of practically applicable solution for the improvement of the personnel safety in automated environments. The study was carried out on the basis of Features of modern work processes in automated environments and utilizing modern machine learning technologies. The subject of the thesis: development of a solution for advanced path-finding in automated environments based on reinforcement learning. Research methods: comparative analysis, analogy method, modeling, business process modeling. The main results of the study: -Â the conducted research has shown the need in safety improving in automated environments; -Â the paper presented the result of the practical application modern machine learning technologies for the creation of an algorithm with an aim to improve safety of the workers; -Â practical application of the proposed solution in the environment of an automated warehouse. Scope of results IT projects in the field of warehouse logistics. The scientific novelty of research is the application of modern technologies of machine learning, linking the creation of the digital environment, Actor-Critic neural network structure and use of Proximal Policy Optimization to ensure stability of the efficiency of the model. Conclusions. The tasks of the thesis have been solved, the goal has been achieved, the scope of the results has been determined.