Comparison of policy functions from the optimal learning and adaptive control frameworks

Autor:	Hans M. Amman, David A. Kendrick
Přispěvatelé:	Equilibrium, Expectations & Dynamics / CeNDEF (ASE, FEB)
Rok vydání:	2014
Předmět:	Mathematical optimization Adaptive control Computer science business.industry Active learning (machine learning) Monte Carlo method Control (management) Function (mathematics) Optimal control Management Information Systems Software Control theory Stochastic optimization business Information Systems
Zdroj:	Computational Management Science, 11(3), 221-235. Springer Verlag
ISSN:	1619-6988 1619-697X
DOI:	10.1007/s10287-014-0215-9
Popis:	In this paper we turn our attention to comparing the policy function obtained by Beck and Wieland (J Econ Dyn Control 26:1359-1377, 2002) to the one obtained with adaptive control methods. It is an integral part of the optimal learning method used by Beck and Wieland to obtain a policy function that provides the optimal control as a feedback function of the state of the system. However, computing this function is not necessary when doing Monte Carlo experiments with adaptive control methods. Therefore, we have modified our software in order to obtain the policy function for comparison to the BW results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bb1c530e94ec2ba94a5e90d5f0922d5 https://doi.org/10.1007/s10287-014-0215-9 Zobrazit plný text záznamu Full text from SpringerLink