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.
文章建议通过强化形成性考核的评价功能，利用多媒体系统提高评价系统的效率以及实施多元评价，来改进和提高远程英语学习者的学习效果。
The article advises on promoting English learning by strengthening the functions of fixed assessment, improving the efficiency through multi-media and enforcing multi-element assessment.
为了提高智能体系统中的典型的强化学习——Q -学习的学习速度和收敛速度，使学习过程充分利用环境信息，本文提出了一种基于经验知识的Q -学习算法。
In order to enhance the study speed and the convergence rate of Q-learning algorithm, an algorithm that based on the experience knowledge about environment is proposed.
应用推荐