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- Reinforcement learning theory and approaches are applied to JLQ model and Q function-based policy iteration algorithm is designed to optimize system performance. 将强化学习的理论和方法应用于JLQ模型，设计基于Q函数的策略迭代算法，以优化系统性能。
- reinforcement learning theory 强化学习理论
- It is no use learning theory without practice. 学习理论而不付诸于实践是没用的。
- Learning Theory and the Symbolic. 学习理论与行为
- But reinforcement learning is bedeviled by the curse of dimensionality. 但强化学习方法一直被维数灾难所困扰。
- The fuzzy neural network (FNN) and reinforcement learning (RL) are integrated. 将模糊神经网络与强化学习理论相结合，构成模糊强化系统。
- We consider it of no use learning theory without practice . 我们认为学习理论而不实践是无用的。
- Learning theory knowledge by group discussion and explaination. 以分组讨论，讲解来进行理论知识。
- "At its core, transformative learning theory is elegantly simple. “它的核心，迁移理论学极为简单。
- This paper adopts reinforcement learning method to accomplish robot soccer cooperation strategy. 利用强化学习方法实现足球机器人协作策略。
- It improves the reinforcement learning method to adapt for the multi-agent learning environment. 它改进了传统的单智能体增强式学习方法，以适应多智能体环境的智能学习。
- ANNs are frequently used in reinforcement learning as part of the overall algorithm. 类神经网络被用在增强式学习整体演算法的一部分。
- A dynamic and real-time system integrating reinforcement learning with simulation is designed for job-shop scheduling. 摘要设计了一个强化学习和仿真相结合的动态实时车间作业排序系统。
- And a constructivist learning theory, to read texts from the teaching methods. 并且以建构主义学习理论为基础，提出自读课文的教学方法。
- Vapnik V N.The Nature of Statistical Learning Theory [M].NY:Springer Verlag. 边肇祺;张学工.;模式识别[M]
- As one of machine learning, reinforcement learning not only has this function but also can expand the acquisitive resource. 作为机器学习的一种方法，增强学习恰可使知识的获取过程自动化，并扩展所能得到的知识资源范围。
- Tasks that fall within the paradigm of reinforcement learning are control problems, games and other sequential decision making tasks. 属于增强式学习的典范的工作是控制问题、赛局理论和其他连续的决策制定工作。
- The reinforcement learning algorithm was also introduced, since it has some relations with the colony algorithm and can be need in the problem of scheduling. 由于蚁群算法与人工智能中的强化学习算法之间有着某种联系，同时强化学习近年来也应用于求解调度问题，因此本文也涉及到了一些强化学习的主要算法。
- It has been shown that the method has more accurate forecasts,reinforcement learning properties and mapping capabilities by comparing with LPM,Fisher model and Logistic model. 通过与多元线性回归模型、Fisher模型和Logistic回归模型的预测结果对比表明,该方法具有预测精度高,学习与泛化能力强,适应性广的优点。
- An algorithm is presented through using structural risk minimization (SRM) based on statistical learning theory. 在研究统计学理论的基础上，提出了以结构风险最小化为目标的训练方法。
