Multi-Agent Distributed Cooperation Decision Making Based on Incomplete Information Prediction

In order to solve the problem of difficulty in learning cooperative strategies and decision-making of multi-agent caused by the incomplete information and the inability to guarantee stable communication in the partial observable multi-agent adversarial environment, inspired by the learning and reasoning functions of the human cerebral cortex through memory, a new multi-agent distributed cooperative strategy learning method based on incomplete information prediction in a partially observable adversarial environment is proposed. Through the use of historical memory and current observation information, support vector regression (SVR) is used to predict invisible information in the environment, and the prediction information of the unobserved part and the observed information are fused as the basis for cooperative strategy learning and decision-making. Then, through the use of decentralized multi-agent reinforcement learning method for cooperative strategy learning, the decision model of each agent in the team is obtained. The method is verified by multi-agent cooperative strategy learning and decision-making in a typical partial observable adversarial environment. The results show that this method can significantly improve the level of multi-agent cooperative decision-making while ensuring high prediction accuracy for unobserved parts.