Learning is of great importance in reinforcement learning.
学习是一种重要的强化学习算法。
Without reinforcement learning is only short term and easily lost.
没有巩固的学习只能是短期的，很快遗忘的。
Can you explain the A. I. technique called reinforcement learning?
你能解释一下什么是“强化学习”技术吗？
What makes a task more appropriate for incorporating reinforcement learning?
什么样的任务更适合应用强化学习技术？
What are the differences between supervised learning and reinforcement learning?
监督学习与强化学习的区别是什么？
This sample graph is from a simple reinforcement learning application that USES Q learning.
这个示例图是从使用Q学习的一个简单增强式学习应用程序中得到的。
Contemporary theories of reinforcement learning are rooted in the dopaminergic reward system.
当代的强化学习理论是基于多巴胺奖赏系统。
The former one is a new approach combining reinforcement learning with feedback control.
基于强化学习的多指手控制方法，该方法将反馈控制与强化学习相结合。
Reinforcement learning (RL) to motion planning of dynamic manipulation tasks was applied.
提出增强学习（RL）解决机器人动态操作任务运动规划的方法。
An average reward reinforcement learning algorithm for control Markov chains is presented.
讨论平均准则控制马氏链的强化学习算法。
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。
MAXQ, a hierarchical reinforcement learning method for multi-agent system, is proposed in recent years.
MAXQ分层多智能体学习方法是近年来被提出的一种新方法。
Simulation machine car through reinforcement learning algorithm, learning optimal navigation strategies.
说明：模拟智能机器小车，通过强化学习算法，学习最优导航策略。
Research on local path planning of mobile robot based on Q reinforcement learning and CMAC neural networks.
基于Q强化学习与CMAC神经网络的移动机器人局部路径规划研究。
Several approaches applying reinforcement learning techniques to game playing have been described in the literature.
将强化学习技术运用于游戏的集中方法在文献里都有记载。
This paper discusses reinforcement learning(RL)algorithm and its application to technical action learning of soccer robot.
主要研究了强化学习算法及其在机器人足球比赛技术动作学习问题中的应用。
The thesis mainly focuses on the dynamic scheduling method based on the averaged rewards reinforcement learning algorithms.
论文主要研究了基于平均型强化学习算法的动态调度方法。
Reinforcement learning has the ability to learn from experience as opposed to supervised learning which learns from examples.
与监督学习从范例中学习的方式不同，强化学习不需要先验知识，而是具有从经验中学习的能力。
For vector control AC drive system, the thesis presented a fuzzy neural network speed controller based on reinforcement learning.
针对矢量控制交流调速系统，该文提出并设计了一种基于再励学习的模糊神经网络速度控制器。
It is rational to adopt the average reward reinforcement learning algorithms for solving the absorbing goal states cyclical tasks.
对于有吸收目标状态的循环任务，比较合理的方法是采用基于平均报酬模型的强化学习。
A reinforcement learning algorithm based on process reward and prioritized sweeping is presented as interference solving strategy.
本文提出了基于过程奖赏和优先扫除的强化学习算法作为多机器人系统的冲突消解策略。
On the basis of theoretical analysis, the cooperative game reinforcement learning method is proposed and its convergence is proved.
在理论分析的基础上，提出了协同博弈的强化学习算法，并证明了算法的收敛性。
Reinforcement learning is an important machine learning method. However, slow convergence has been one of main problem in practice.
强化学习是一种重要的机器学习方法，然而在实际应用中，收敛速度缓慢是其主要不足之一。
Reinforcement learning based on Markov decision process is a way of on-line learning, which can be applied to single agent environment.
基于马尔科夫过程的强化学习作为一种在线学习方式，能够很好地应用于单智能体环境中。
This characteristic of reinforcement learning must increase learning difficulty for intelligent system and learning time also grows up.
强化学习的这种特性必然增加智能系统的困难性，学习时间增长。
In this paper, the approximate theorem of average reward reinforcement learning is proven by means of the theory of performance potentials.
文中基于性能势理论，证明了平均奖赏强化学习的逼近定理。
Reinforcement learning is a common technique for this scenario as well as the more traditional scenario of actually learning the utility function.
强化学习是这种情况下的常用技术，而更多的传统情形下需要使用效用函数。
Here the computational principle is reinforcement learning and active exploration, which may also be behind learning motor movements in an infant.
在这里计算原理是加强学习过程和主动探索过程，这些也许也是婴儿学习动机背后的原因。
Reinforcement learning does not need priori knowledge and improves its behavior policy with knowledge obtained by interaction with the environment.
强化学习不需要有先验知识，而是通过与环境的不断交互获得知识，改进行为策略，具有自学习的能力。
Reinforcement learning does not need priori knowledge and improves its behavior policy with knowledge obtained by interaction with the environment.
强化学习不需要有先验知识，而是通过与环境的不断交互获得知识，改进行为策略，具有自学习的能力。
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