基于改进Q-学习算法的多阶段群体决策模型

多阶段群体决策问题是一类典型的动态群体决策问题,主要针对离散的确定状态下的最优群体决策问题求解.但由于现实环境面临的大部分是不确定状态空间,甚至是未知环境空间(例如状态转移概率矩阵完全未知),为了寻求具有较高共识度的多阶段群体最优策略,决策者需要通过对环境的动态交互来获得进一步的信息.针对该问题,利用强化学习技术,提出一种求解多阶段群体决策的最优决策算法,以解决在不确定状态空间下的多阶段群体决策问题.结合强化学习中的Q-学习算法,建立多阶段群体决策Q-学习基本算法模型,并改进该算法的迭代过程,从中学习得到群体最优策略.同时证明基于Q-学习得到的多阶段群体最优策略也是群体共识度最高的策略.最后,通过一个计算实例说明算法的合理性及可行性.

A multi-stage group decision model based on improved Q-learning

The multi-stage group decision making problem is a typical sequential group decision making problem. It is normally utilized to find the optimal solution to the group decision problems in discrete deterministic environment. However, the real life environments faced by decision-makers are usually full of uncertainty, even unknown environments (with unknown state transition matrix). Therefore, it is essential for the decision-makers to obtain more information by interacting with the environment dynamically to achieve an optimal decision strategy with high consensus degree. Due to the advantage of reinforcement learning in handling the sequential decision-making problems, the classical reinforcement learning algorithm (Q-learning) is improved to discover the optimal solution of multi-stage group decision making problems under uncertain environment. Additionally, a theorem is proposed to show that the optimal group decision obtained by using the improved Q-learning algorithm is the group decision with the highest degree of group consensus. Finally, an illustrative example is presented to verify the rationality and feasibility of the proposed algorithm.