中文摘要：

针对认知无线电多用户的信道和功率资源分配问题，提出一种基于用户聚类和可变学习速率的多Agent强化学习方法．首先使用分层处理分离信道选择与功率控制，采用快速最优搜索结合用户数均衡调节实现信道分配；其次，使用随机博弈框架对多用户功率控制问题进行建模，通过K均值用户聚类减少博弈参与用户数量和降低单个用户的环境复杂度，并使用可变Q学习速率和策略学习速率的方法进一步促进多Agent强化学习的收敛．仿真结果表明，该方法能使多个用户的功率状态和总收益有效收敛，并且使整体性能达到次优．

英文摘要：

A multi-agent enforcement learning method based on user clustering as well as a variable learning rate was proposed for solving the problem of channel allocation and power control within multi cognitive radio users. Firstly, a hierarchy processing method was used to separate channel selection and power control. The channel allocation was implemented by fast optimal search combined with user-num- ber balance. Secondly, stochastic game framework was adopted to model the muhiuser power control is- sue. In subsequent multi-agent enforcement learning, K-means user clustering method was employed to reduce the user number in game and single user＇ s environment complexity, and a variable learning rate scheme for Q learning and policy learning was proposed to promote the convergence of muhiuser learning. Simulation shows that the method can make multiuser＇ s power status and global reward converging effec- tively, moreover the whole performance can reach sub-optimal.