  One of the key tasks in the economy is forecasting the economic agents'
expectations of the future values of economic variables using mathematical
models. The behavior of mathematical models can be irregular, including
chaotic, which reduces their predictive power. In this paper, we study the
regimes of behavior of two economic models and identify irregular dynamics in
them. Using these models as an example, we demonstrate the effectiveness of
evolutionary algorithms and the continuous deep Q-learning method in
combination with Pyragas control method for deriving a control action that
stabilizes unstable periodic trajectories and suppresses chaotic dynamics. We
compare qualitative and quantitative characteristics of the model's dynamics
before and after applying control and verify the obtained results by numerical
simulation. Proposed approach can improve the reliability of forecasting and
tuning of the economic mechanism to achieve maximum decision-making efficiency.
