【摘要】 为了提高MPOMDP模型的知识表示能力和推理效率,提出一种基于Agent内部结构的MPOMDP模型。该模型能表示Agent的内部结构及其时间演化,并通过将系统联合概率分布表示成每个Agent内部变量集的局部因式形式,以提高模型的推理效率。将GPI-POMDP算法扩展到基于内部结构的MPOMDP模型中,给出基于内部状态的多Agent策略梯度算法(MIS-GPOMDP),来求解基于内部结构的MPOMDP。实验结果表明MIS-GPOMDP算法具有较高的推理效率,且算法是收敛的。更多还原

【Abstract】 For the improvement of knowledge representation ability and reasoning efficiency of MPOMDP model,a new kind of MPOMDP model is proposed based on internal structure of Agent.The internal structure and its evolvement of Agent are presented to improve the reasoning efficiency of the model by means the joint probability distribution of system as the local factorization forms of internal variables set.A MIS-GPOMDP algorithm is given by expanding GPI-POMDP to internal structural MPOMDP model to solve the internal structural MPOMDP model.The results of the experiment show that the high efficiency of the reasoning and convergence are found in MIS-GPOMDP algorithm更多还原