The thesis addresses the problem of closed-loop speed control of a DC motor with variable load using the reinforcement learning method. Classical methods, such as PID control, often fail to provide optimal results in nonlinear and dynamically changing systems. Therefore, the thesis explores the potential application of artificial intelligence. A key part of the research focuses on evaluating the impact of different combinations of observations on the learning efficiency and performance of the controller.
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