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中文摘要：
基于群体环境中个体agent局部感知和交互的生物原型，提出一种随机对策框架下的多agent局部学习算法．算法在与局部环境交互中采用贪婪策略最大化自身利益．分别在零和、一般和的单个平衡点和多个平衡点情形下改进了Nash-Q学习算法；提出了行为修正方法，并证明了算法收敛、计算复杂度降低．
英文摘要：
A local learning algorithm for multi-agent-based stochastic games is proposed in light of the fact that the individual performs local perception and interaction in group. In the algorithm, every agent adopts greedy policy to maximize- its payoff when interacting with the environment. The Nash-Q earning algorithm is improved respectively in situations of zero-sum, general-sum games with only one equilibrium or multi-equilibrium. Besides, the method to modify the behavior is proposed, and it is proved that the algorithm is convergent and the computing complexity is reduced.
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