
The core message of this paper is to develop an optimal control theory for stochastic reaction networks, which is an important problem with significant implications for the control of biological systems. The authors provide a comprehensive analysis of the continuous-time and sampled-data optimal control problems for stochastic reaction networks, deriving the optimal control laws and characterizing them in terms of Hamilton-Jacobi-Bellman equations and Riccati differential equations.


coremsg

optimal-control-of-stochastic-reaction-networks-with-mass-action-kinetics


Optimal Control of Stochastic Reaction Networks with Mass-Action Kinetics


title_rewrite


The authors propose a new framework for optimal control of stochastic reaction networks using a control cost function based on Kullback-Leibler divergence, which allows for efficient computation of optimal solutions by linearizing the Hamilton-Jacobi-Bellman equation.


optimal-control-of-stochastic-reaction-networks-with-entropic-control-cost-and-emergence-of-mode-switching-strategies


Optimal Control of Stochastic Reaction Networks with Entropic Control Cost and Emergence of Mode-Switching Strategies
