强化 学习 英语是什么意思 - 英语翻译

• reinforcement learning
• RL
• reinforced learning
• intensive learning
• of intensive study
• reinforcement learning-based
• learning enrichment

在 中文 中使用 强化 学习 的示例及其翻译为 英语

强化学习-Qlearning.
Reinforced learning, see Q-learning;

强化学习的经典应用是玩游戏。
The classic application of reinforced learning is game playing.

两年的教师强化学习经历包括以下关键阶段：.
The two-year, intensive learning experience for faculty includes the following key phases:.

强化学习的经典应用是游戏。
The classic application of reinforced learning is game playing.

强化学习的经典应用就是玩游戏。
The classic application of reinforced learning is game playing.

深入和强化学习.
Deep and Reinforced Learning.

基于模型的深度强化学习（涉及到无监管预测型学习）。
Deep model-based reinforcement learning(which involved unsupervised predictive learning).

不过，强化学习agent可能会需要。
A reinforcement learning agent might, though.

强化学习和贝叶斯方法之间的联系。
The connection between Reinforcement Learning and Bayesian methods.

这就是对一个强化学习问题的简单描述。
This is a simplified description of a reinforcement learning problem.

最后一章讨论了强化学习对未来社会的影响。
The final chapter discusses the future societal impacts of reinforcement learning.

强化学习与其他机器学习不同之处为:.
The main difference between the reinforcement learning and other machine learning methods are:.

强化学习主体的目标，是得到尽可能多的奖励。
The goal of a reinforcement learning agent is to collect as much reward as possible.

强化学习策略是正确的。
The Enhanced Learning Strategy is well in place.

强化学习有两个元素:Agent和环境（Environment）。
Two major components are there in reinforcement learning: Agent and the environment.

强化学习问题可以通过游戏来最好地解释。
A Reinforcement Learning problem can be best explained through games.

图3.1：强化学习中智能体与环境的交互.
Figure 3.1: The agent-environment interaction in reinforcement learning.

了强化学习和人工智能实验.
The Reinforcement Learning and Artificial Intelligence Laboratory.

Alphago是强化学习系统，具有某些不同寻常的特征。
AlphaGo is a reinforcement-learning system with some unusual features.

然而强化学习并不知道这个!
However, in reinforcement learning we don't know these!

强化学习的主体与环境基于离散的时间步长相作用。
A reinforcement learning agent interacts with its environment in discrete time steps.

强化学习包括时间延迟和稀疏标签-未来的奖励。
The reinforcement learning consists of time-delayed and sparse labels- the future rewards.

强化学习会议.
The Multi-disciplinary Conference on Reinforcement Learning.

这可以说是强化学习和监督学习的主要区别。
This is the main difference that can be said of reinforcement learning and supervised learning..

简单随机搜索提供种强化学习竞争方」一.
The" Simple random search provides a competitive approach to reinforcement learning.

萨顿成为强化学习的主要倡导者。
Sutton went on to become the leading proponent of reinforcement learning.

AlphaGo是一个强化学习系统，但却有着一些不同寻常的特征。
AlphaGo is a reinforcement-learning system with some unusual features.

收益信号定义了强化学习问题的目标。
A reward signal defines the goal in a reinforcement learning problem.

这是强化学习的基础。
This is the basis of reinforced learning.

现在回到强化学习。
Now, coming back to Reinforcement Learning.