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。
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？
I think that's an example of where reinforcement learning is maybe not quite mature enough to really operate in these incredibly unconstrained environments where the reward signals are less crisp.
我认为这个例子就能说明强化学习技术可能还不够成熟，在这种奖励信号不够明确、约束条件太少的环境下，还不能真正有效地运行。
Is Google incorporating reinforcement learning in the core search product?
谷歌已经开始将强化学习技术用在核心搜索产品上了吗？
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？
Tis paper presents two-layer reinforcement learning method for multi-agent cooperation.
提出了多智能体协作的两层强化学习方法。
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。
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。
The BP neural network is adopted to realize the reinforcement learning to strengthen the generalization ability.
应用BP神经网络实现强化学习，以增强系统的泛化能力；
Q learning algorithm is the most popular reinforcement learning algorithm, but the algorithm exist some problems.
目前主流的强化学习算法是Q学习算法，但Q学习本身存在一些问题。
Hierarchical reinforcement learning is one of the important methods for solving large scale learning space problems.
分层式强化学习是解决强化学习问题中大规模学习空间问题的一种重要方法。
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。
Hierarchical reinforcement learning (HRL) was presented to combat the curse of dimensionality, and has made great progresses.
分层强化学习(HRL)是为解决强化学习的维数灾问题而提出的，并取得了显著进展。
I think where reinforcement learning has some challenges is when the action-state you may take is incredibly broad and large.
我觉得强化学习面临的一些挑战主要集中在当你可以采取的行为状态极为宽泛的时候。
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。
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。
Therefore, this study provides the evidence of reinforcement learning theory to explain FRN and extends the functional significance of P300.
所以本研究为FRN的强化学习理论解释提供了依据，并且扩展了P300的功能意义。
A neural network controller is designed based on reinforcement learning neural network framework to achieve adaptive control of velocity tracking.
然后基于强化学习神经网络结构设计神经网络控制器以取得车速跟踪的自适应控制。
The paper proposes a model of reinforcement learning based on ant colony algorithm, namely the combination of ant colony algorithm and Q learning.
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。
To solve the robots path planning problem in dynamic environment, this paper applies adaptive learning to path planning based on reinforcement learning.
本文结合机器人路径规划问题介绍了增强式学习方法，实现了动态环境中基于增强式学习的自适应路径规划。
The state entropy is proposed for reinforcement learning as the measurement of the state's uncertainty caused by the incompletion of the observed information.
提出强化学习中的状态熵，来描述观测状态信息不完整引起的不确定性；
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