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 neuro-evolution of augmenting topologies to solve control automation problems using modeling control problems of technical systems. Key application components include OpenAI Gym toolkit for develop-ing and comparing reinforcement learn-ing algorithms, full-fledged open-source implementation of the NEAT genetic al-gorithm called SharpNEAT, and inter-mediate software for orchestration of these components. The algorithm of neu-roevolution of augmenting topologies demonstrates the finding of efficient neural networks on the example of a simple standard problem with continu-ous control from OpenAI Gym.