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中文摘要：
基于Q-强化学习算法，建立了进化博弈中代理人的决策模型．考虑到强化学习算法不需要建立环境模型，可用于不完全、不确定信息问题，将Q-强化学习算法引入到进化博弈中，研究了进化博弈中两种Q-学习决策模型：单代理人Q-学习决策模型和多代理人Q-学习决策模型，并针对不同结构的进化博弈选择不同的决策模型和算法进行了讨论．仿真算例的结果说明基于Q-学习的决策模型能指导代理人学习、选择最优策略．
英文摘要：
Based on Q-reinforcement learning, decision-models of agents in evolutionary games are established. Considering that the reinforcement learning does not need a model of its environment, and it can be used in problems with incomplete and uncertain information, Q-learning is introduced to evolutionary games; and the single-agent Q-learning decision-model and the multi-agent Q- presented. In addition, how to choose decision-models and algorithms accord earning decision-model are ng to the type of games is discussed. The results of simulation experiments show that the decision-models based on Q-learning can make agents to choose the optimal strategy by learning.
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