Application of neuro evolution tools in automation of technical control systems
Reinforced learning is a field of machine learning based on how software agents should perform actions in the environment to maximize the concept of cumulative reward. This paper proposes a new application of machine reinforcement learning techniques in the form of neuroevolution of augmenting topol...
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| Дата: | 2021 |
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| Автори: | , |
| Формат: | Стаття |
| Мова: | Ukrainian |
| Опубліковано: |
Інститут програмних систем НАН України
2021
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| Теми: | |
| Онлайн доступ: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/448 |
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| Назва журналу: | Problems in programming |
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Репозитарії
Problems in programming| Резюме: | Reinforced learning is a field of machine learning based on how software agents should perform actions in the environment to maximize the concept of cumulative reward. This paper proposes a new application of machine reinforcement learning techniques in the form of neuroevolution of augmenting topologies to solve control automation problems using modeling control problems of technical systems. Key application components include OpenAI Gym toolkit for developing and comparing reinforcement learning algorithms, full-fledged open-source implementation of the NEAT genetic algorithm called SharpNEAT, and intermediate software for orchestration of these components. The algorithm of neuroevolution of augmenting topologies demonstrates the finding of efficient neural networks on the example of a simple standard problem with continuous control from OpenAI Gym.Prombles in programming 2021; 1: 16-25 |
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