Optimal Regulation Strategy for Nonzero-Sum Games of the Immune System Using Adaptive Dynamic Programming

This article investigates the optimal control strategy problem for nonzero-sum games of the immune system based on adaptive dynamic programming (ADP). First, the main objective is approximating a Nash equilibrium between the tumor cells and the immune cell population, which is governed through chemotherapy drugs and immunoagents guided by the mathematical growth model of the tumor cells. Second, a novel intelligent nonzero-sum games-based ADP is put forward to solve the optimization control problem by reducing the growth rate of tumor cells and minimizing chemotherapy drugs and immunotherapy drugs. Meanwhile, the convergence analysis and iterative ADP algorithm are specified to prove feasibility. Finally, simulation examples are listed to account for availability and effectiveness of the research methodology.

Automated summary

The article investigates the optimal control strategy problem for nonzero-sum games of the immune system based on adaptive dynamic programming. It aims to approximate a Nash equilibrium between tumor cells and the immune cell population by using chemotherapy drugs and immunoagents. A novel intelligent nonzero-sum games-based ADP method is proposed to reduce the growth rate of tumor cells and minimize the usage of chemotherapy drugs and immunotherapy drugs. The feasibility of this approach is proven through convergence analysis and an iterative ADP algorithm. Simulation examples are provided to demonstrate the availability and effectiveness of the research methodology.