关键词: 强化学习，Meta平衡，NashQ，多Agent系统

Abstract: Multi-agent reinforcement learning algorithms aim at cooperation strategy, while NashQ is frectuently menboned as a pivotal algorithm to the study of non-cooperative strategics. In multi agent systems, Nash equilibrium can not ensure the solutions obtained Pareto optimal, besides, the algorithm with high computation complexity. MetaQ algorithm was proposed in this paper. It is different from NashQ that MetaQ finds out the optimal solution by the pretreatment of its own behavior and the prediction of the others behavior. In the end,a game-climate cooperation strategy was used in this paper, and the results shows that MetaQ algorithm, with impressive performance, is fit for non-cooperative problem.

Key words: Reinforcement learning, Meta ectuilibrium, NashQ, Multi-agent system