The Effectiveness of the A2C Algorithm in Relation to Classical Models of the Theory of Economic Growth

The relevance of the study is to identify the accuracy of the estimate obtained by the A2C algorithm, as well as the need for verification of reinforcement learning when working with optimization of economic processes. The purpose of the study was to analyze the effectiveness of the A2C algorithm, along with the specifics of its implementation, in solving optimization economic problems. The tasks considered were maximizing consumption in the Solow, Romer and Schumpeterian models of endogenous economic growth, and maximizing per capita income in the latter two, according to the consumption rate (in the latter two – saving rate) and the share of scientists in the economy, respectively. The results showed that for deterministic models (Solow model, Romer model), the variance of the parameter estimate is minimal and the average differs from the value obtained analytically by no more than a thousandth part with a sufficiently high number of time periods in the model. Nevertheless, in stochastic models (the Schumpeterian model), firstly, a high number of time periods in the model is required to match the estimate to the value obtained analytically, and secondly, the estimate obtained in this way, although biased by no more than a thousandth of a fraction, has a high variance.