Reinforcement Learning Model and Algorithm Based on Multi-agent Cooperation
多Agent协作的强化学习模型和算法

The multi-agent cooperative learning process based on Reinforcement Learning is addressed and a new multi-agent cooperative learning model is proposed. Based on this model, a cooperative learning algorithm is introduced. This algorithm pays fully attention to multi-agent cooperative learning together simultaneity, so it can make each agent predict its action policy based on the estimation on its action's long-time reward. At last relevant decisions to be the best associated action policy is made. We conduct a series of empirical evaluation of the algorithm on the hunter-prey problem to validate its astringency. The result shows this algorithm is an efficient and fast method for multi-agent learning.