Application of neuro evolution tools in automation of technical control systems

А.Yu. Doroshenko, P.Z. Achour


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


artificial neural networks; reinforced learning; genetic algorithms; control automation in technical systems

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Haykin, Simon S. (1999). Neural networks: a comprehensive foundation. Prentice Hall.

Wilson, Halsey (2018). Artificial intelligence. Grey House Publishing. 184 pages.

Kenneth O. Stanley. Ph.D. Dissertation: EFFICIENT EVOLUTION OF NEURAL NETWORKS THROUGH COMPLEXIFICATION / Kenneth O. Stanley // Department of Computer Sciences, The University of Texas at Austin. – 2004. –

Russell, Stuart J.; Norvig, Peter (2010). Artificial intelligence: a modern approach (Third ed.). Upper Saddle River, New Jersey. P. 830, 831.

Kenneth O. Stanley and Risto Miikkulainen (2002). "Evolving Neural Networks Through Augmenting Topologies". Evolutionary Computation 10 (2): 99-127 CrossRef



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