多智能体分时跟踪学习

hought MAS has been applied in many fields, dimensions and the communications problem always are the big challenges to be faced and taken a breakthrough in the cooperative multi-agent system, especially in large-scale space. In this paper the multi-agent algorithms of join-state single-action Q-learning (JSQ-learning) and JSQ-learning based policy prediction (JSQL-PP) are proposed from original definition of reinforcement learning to reduce the dimensions of the large-scale space and learn without communications in MAS. And both convergences of two algorithms are verified. Then, a multi-agent cooperative learning framework is built to make every agent has chances to improve the cooperative strategy with time-tracking. The learning algorithm JSQL-PP is extended to an algorithm for learning a better policy in the proposed framework (JSQL-TT). Our new algorithm can be shown to get a better strategy rapidly in large-scale space in experiments of two agents dating in the grid word and three agents' line up problem. And the comparison results of the Q-learning and JSQL-TT have shown the effectiveness and superiority of our time-tracking framework.