The Effectiveness of the A2C Algorithm in Relation to Classical Models of the Theory of Economic Growth
( Pp. 68-77)
More about authors
Moiseenko Alexander M.
1st year graduate student, Department of System Analysis
Russian Academy of National Economy and Public Administration under the President of the Russian Federation
Moscow, Russian Federation Grineva Natalia V. Cand. Sci. (Econ.), Associate Professor; associate professor, Department of Data Analysis and Machine Learning; Financial University under the Government of the Russian Federation; Moscow, Russian Federation
Russian Academy of National Economy and Public Administration under the President of the Russian Federation
Moscow, Russian Federation Grineva Natalia V. Cand. Sci. (Econ.), Associate Professor; associate professor, Department of Data Analysis and Machine Learning; Financial University under the Government of the Russian Federation; Moscow, Russian Federation
Abstract:
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.
How to Cite:
Moiseenko A.M., Grineva N.V. The Effectiveness of the A2C Algorithm in Relation to Classical Models of the Theory of Economic Growth. Computational Nanotechnology. 2024. Vol. 11. No. 1. Pp. 68–77. (In Rus.) DOI: 10.33693/2313-223X-2024-11-1-68-77. EDN: DVOVHU
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Zheng S. et al. The ai economist: Improving equality and productivity with AI-driven tax policies // arXiv preprint arXiv:2004.13332. 2020.
Didenko D.V., Grineva N.V. Factors of economic growth in the late USSR in a spatial perspective. Economic Policy. 2022. Vol. 17. No. 2. (In Rus.) Pp. 88–119. EDN: MBEJDX. DOI: 10.18288/1994-5124-2022-2-88-119.
Grineva N.V. Assessment of intellectual capital during the transition to a digital economy. Problems of Economics and Legal Practice. 2022. Vol. 18. No. 2. Pp. 219–227. (In Rus.) EDN: CGWWNJ.
Krinichansky K., Grineva N. Dynamic approach to the analysis of financial structure: Overcoming the bank-based vs market-based dichotomy. In: 16th International Conference Management of large-scale system development (MLSD). 2023. No. 16. EDN: RSHSND. DOI: 10.1109/MLSD58227.2023.10303933.
Keywords:
reinforcement learning, macroeconomic modeling, Solow model, Romer model, Schumpeterian model of endogenous economic growth, optimization of macroeconomic processes, theory of economic growth.
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