Kullback–Leibler Control in Boolean Control Networks
研究了布尔控制网络中的KL控制问题，提出一种考虑控制输入的扩展阶段成本函数，并给出矩阵迭代算法，该算法可近似传统动态规划。
This article addresses the Kullback-Leibler (KL) control problem in Boolean control networks. In the considered problem, an extended stage cost function depending on the control inputs is introduced; in contrast to a stage cost of the conventional KL control problems in the Markov decision process cannot take into consideration the control inputs. An associated Bellman equation and a matrix-based iteration algorithm are presented. The theoretical analysis shows that the proposed KL control results in an approximated form of conventional dynamic programming (DP). Furthermore, the convergence analysis is presented, with the weight parameter converging to zero and diverging to infinity. In practical application examples, a comparison of the conventional DP and proposed KL control is illustrated.