Adaptive dynamic programming for the optimal liver regeneration control - Karieva, Valeriia; Lvov, Sergey (Visnyk of V. N. Karazin Kharkiv National University. Ser. Mathematics, Applied Mathematics and Mechanics, 2024)
Every living organism interacts with an environment and uses that interaction for an improvement of its own adaptability, and, as a result, one’s survival and overall development. The process of evolution shows us that different species change methods of interaction with an environment with passage of time, which leads to natural selection and survival of the most adaptive ones. This learning, which based on actions, or reinforcement learning may embrace the idea of optimal behavior occurring in environmental systems. We describe mathematical formulas for reinforcement learning and the practical integration method also known as adaptive dynamic programming. That gives us the overall concept of controllers for artificial biological systems that both learn and show the optimal behavior.This paper reviews the formulation of the upper optimality problem, for which the optimal regulation strategy is guaranteed to be better or equivalent to objective regulation rules that can be observed in natural biological systems.In cases of optimal reinforcement learning algorithms the learning process itself moves from the analysis of the item take on system dynamics to the much higher level. The object of interest now is not the details of the system dynamics, but the quantity efficiency index, which clearly represents how optimally the control system works. Such scheme of reinforcement learning is learning technique of optimal behavior in order to monitor the response to non-optimal control strategies.The purpose of this article is to show the possibility of using of reinforcement learning methods, the adaptive dynamic programming (ADP) in particular, to control biological systems using feedback. This article shows the on-line methods for solving the problem of searching the upper optimality estimate with adaptive dynamic programming.