Artificial intelligence algorithms have made great strides in recent years with the ability
to solve complex problems. The first applications of this technology began to emerge
in the world of computer science and game theory. As technology evolved, the use has
rapidly spread to other areas - including robotics and control systems. The question
arises whether the new AI approach can bring us closer to usefulness of conventional
methods of controlling large-scale compressed air systems. This thesis presents the
production of a simulation model, testing of the simulation model with comparison to
the real system, the integration of reinforcement learning with the simulation model
and the analysis of the parameters of reinforcement learning in the system. The learned
policy was compared with conventional method which proved the learned policy to be
more successful.
|
