本文提出了一种基于蚁群算法的强化学习模型，即蚁群算法与Q学习相结合的思想。
The paper proposes a model of reinforcement learning based on ant colony algorithm, namely the combination of ant colony algorithm and Q learning.
传统的强化学习模型在整个学习过程中使用恒定学习速率，导致在未知环境下收敛速度慢且适应性差。
The learning process use the constant learning rate in the traditional reinforce learning model, because of that robot learn in a low convergence speed and with the poor adaptation.
由于强化学习理论的限制，在多智能体系统中马尔科夫过程模型不再适用，因此不能把强化学习直接用于多智能体的协作学习问题。
Due to the theoretical limitation that it assumes that an environment is Markovian, traditional reinforcement learning algorithms cannot be applied directly to multi-agent system.
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